BRENDA - Enzyme Database

Classifying nitrilases as aliphatic and aromatic using machine learning technique

Sharma, N.; Verma, R.; Savitri, R.; Bhalla, T.C.; 3 Biotech 8, 68 (2018)

Data extracted from this reference:

Organism
EC Number
Organism
UniProt
Commentary
Textmining
3.5.5.1
Aeribacillus pallidus
-
-
-
3.5.5.1
Arabidopsis thaliana
P46010
-
-
3.5.5.1
Arenibacter latericius
-
-
-
3.5.5.1
Bacillus sp. OxB-1
P82605
-
-
3.5.5.1
Blastomyces dermatitidis
-
-
-
3.5.5.1
Blastomyces dermatitidis ER-3
-
-
-
3.5.5.1
Bordetella bronchiseptica
A0A0H3LIT0
-
-
3.5.5.1
Burkholderia cenocepacia
B4EE44
-
-
3.5.5.1
Burkholderia multivorans
B9BCZ1
-
-
3.5.5.1
Burkholderia multivorans CGD1
B9BCZ1
-
-
3.5.5.1
Burkholderia sp. BT03
-
-
-
3.5.5.1
Butyrivibrio sp. MC2021
-
-
-
3.5.5.1
Calidithermus chliarophilus
-
-
-
3.5.5.1
Cellulophaga algicola DSM 14237
-
-
-
3.5.5.1
Chryseobacterium sp. UNC8MFCol
-
-
-
3.5.5.1
Desulfatibacillum aliphaticivorans
-
-
-
3.5.5.1
Dyadobacter alkalitolerans
-
-
-
3.5.5.1
Flexithrix dorotheae
-
-
-
3.5.5.1
Fodinicurvata sediminis
-
-
-
3.5.5.1
Geodermatophilus obscurus
D2SGH7
-
-
3.5.5.1
Geodermatophilus obscurus ATCC 25078
D2SGH7
-
-
3.5.5.1
Janthinobacterium sp. Marseille
A6T0X3
-
-
3.5.5.1
Maribacter antarcticus
-
-
-
3.5.5.1
Maricaulis maris
-
-
-
3.5.5.1
Maricaulis maris MCS10
-
-
-
3.5.5.1
Marssonina brunnea
-
-
-
3.5.5.1
Marssonina brunnea MB_m1
-
-
-
3.5.5.1
Methanosarcina mazei
Q8PXI9
-
-
3.5.5.1
Methanosarcina mazei BAA-159
Q8PXI9
-
-
3.5.5.1
Microscilla marina
A1ZD79
-
-
3.5.5.1
Microscilla marina ATCC 23134
A1ZD79
-
-
3.5.5.1
Morganella morganii subsp. morganii
-
-
-
3.5.5.1
Morganella morganii subsp. morganii KT
-
-
-
3.5.5.1
Niabella soli
-
-
-
3.5.5.1
Niabella soli DSM 19437
-
-
-
3.5.5.1
Nocardiopsis dassonvillei ATCC 23218
D7B8P3
-
-
3.5.5.1
Pantoea sp. AS-PWVM4
-
-
-
3.5.5.1
Parabacteroides gordonii
-
-
-
3.5.5.1
Pedobacter jeongneungensis
-
-
-
3.5.5.1
Pseudomonas entomophila
Q1I7X1
-
-
3.5.5.1
Pseudomonas entomophila L48
Q1I7X1
-
-
3.5.5.1
Pseudomonas oleovorans
-
-
-
3.5.5.1
Pseudomonas oleovorans CECT:5344
-
-
-
3.5.5.1
Pseudomonas sp. GM41
-
-
-
3.5.5.1
Pseudonocardia spinosispora
-
-
-
3.5.5.1
Rhizobium leguminosarum bv. trifolii
-
-
-
3.5.5.1
Rhizobium leguminosarum bv. trifolii WSM1325
-
-
-
3.5.5.1
Rhodococcus aetherivorans
-
-
-
3.5.5.1
Rubellimicrobium mesophilum
-
-
-
3.5.5.1
Rubellimicrobium mesophilum DSM 19309
-
-
-
3.5.5.1
Runella slithyformis
-
-
-
3.5.5.1
Saccharomyces cerevisiae
B3LU11
-
-
3.5.5.1
Saccharomyces cerevisiae RM11-1a
B3LU11
-
-
3.5.5.1
Scheffersomyces stipitis
A3LXL4
-
-
3.5.5.1
Scheffersomyces stipitis ATCC 58785
A3LXL4
-
-
3.5.5.1
Sediminispirochaeta bajacaliforniensis
-
-
-
3.5.5.1
Shewanella sediminis
A8FQL4
-
-
3.5.5.1
Shewanella sediminis HAW-EB3
A8FQL4
-
-
3.5.5.1
Sphingobacterium thalpophilum
-
-
-
3.5.5.1
Stappia stellulata
-
-
-
3.5.5.1
Streptomyces albus
D6B7V7
-
-
3.5.5.1
Streptomyces albus J1074
D6B7V7
-
-
3.5.5.1
Synechocystis sp. PCC 6803
-
-
-
3.5.5.1
Thalassiosira pseudonana
B8C1M9
-
-
3.5.5.1
Thalassospira lucentensis
-
-
-
3.5.5.1
Tomitella biformata
-
-
-
3.5.5.7
Achromobacter xylosoxidans
-
-
-
3.5.5.7
Acidovorax oryzae
-
-
-
3.5.5.7
Agrobacterium rhizogenes
-
-
-
3.5.5.7
Agrobacterium rhizogenes ATCC 15834
-
-
-
3.5.5.7
Amycolatopsis taiwanensis
-
-
-
3.5.5.7
Azospirillum halopraeferens
-
-
-
3.5.5.7
Betaproteobacteria bacterium
-
-
-
3.5.5.7
Betaproteobacteria bacterium MOLA814
-
-
-
3.5.5.7
Bosea sp. 117
-
-
-
3.5.5.7
Bradyrhizobium elkanii
-
-
-
3.5.5.7
Bradyrhizobium sp. ORS 278
A4YWK0
-
-
3.5.5.7
Bradyrhizobium sp. th.b2
-
-
-
3.5.5.7
Burkholderia gladioli
-
-
-
3.5.5.7
Burkholderia sp. BT03
-
-
-
3.5.5.7
Colletotrichum fioriniae
-
-
-
3.5.5.7
Colletotrichum fioriniae PJ7
-
-
-
3.5.5.7
Comamonas testosteroni
-
-
-
3.5.5.7
Cupriavidus sp. WS
-
-
-
3.5.5.7
Danaus plexippus
-
-
-
3.5.5.7
Danaus plexippus F2
-
-
-
3.5.5.7
Janthinobacterium sp. Marseille
-
-
-
3.5.5.7
Marinomonas ushuaiensis
-
-
-
3.5.5.7
Marinomonas ushuaiensis DSM 15871
-
-
-
3.5.5.7
Mesorhizobium loti
-
-
-
3.5.5.7
Methylibium petroleiphilum
-
-
-
3.5.5.7
Methylobacterium sp. 88A
-
-
-
3.5.5.7
Methylobacterium sp. L2-4
-
-
-
3.5.5.7
Methylopila sp. 73B
-
-
-
3.5.5.7
Methylopila sp. M107
-
-
-
3.5.5.7
Methyloversatilis universalis
-
-
-
3.5.5.7
Nocardia sp. C-14-1
-
-
-
3.5.5.7
Oligotropha carboxidovorans
-
-
-
3.5.5.7
Oligotropha carboxidovorans OM5
-
-
-
3.5.5.7
Paraburkholderia kururiensis
-
-
-
3.5.5.7
Paraburkholderia mimosarum
-
-
-
3.5.5.7
Polycyclovorans algicola
-
-
-
3.5.5.7
Pseudomonas syringae pv. syringae
Q500U1
-
-
3.5.5.7
Pseudomonas syringae pv. syringae B728a
Q500U1
-
-
3.5.5.7
Rhizobium leguminosarum
-
-
-
3.5.5.7
Rhizobium leguminosarum bv. viciae 3841
-
-
-
3.5.5.7
Rhizobium sp. JGI 0001019-L19
-
-
-
3.5.5.7
Rhizoctonia solani
-
-
-
3.5.5.7
Rhizoctonia solani 123E
-
-
-
3.5.5.7
Rhodococcus rhodochrous
-
-
-
3.5.5.7
Rhodococcus rhodochrous J1
-
-
-
3.5.5.7
Rhodococcus rhodochrous K22
-
-
-
3.5.5.7
Saccharomonospora viridis
-
-
-
3.5.5.7
Saccharomonospora viridis DSM 43017
-
-
-
3.5.5.7
Serratia sp. M24T3
-
-
-
3.5.5.7
Shimwellia blattae
I2BBF1
-
-
3.5.5.7
Shimwellia blattae ATCC 29907
I2BBF1
-
-
3.5.5.7
Sorangium cellulosum
-
-
-
3.5.5.7
Sorangium cellulosum So0157-2
-
-
-
3.5.5.7
Sphingopyxis alaskensis
Q1GTC0
-
-
3.5.5.7
Sphingopyxis alaskensis DSM 13593
Q1GTC0
-
-
3.5.5.7
Starkeya novella
-
-
-
3.5.5.7
Synechococcus elongatus PCC 6301
Q31PZ9
-
-
3.5.5.7
Teredinibacter turnerae
-
-
-
3.5.5.7
Variovorax paradoxus
-
-
-
3.5.5.7
Variovorax paradoxus EPS
-
-
-
3.5.5.7
Variovorax sp. P21
-
-
-
3.5.5.7
Xanthobacter sp. 126
-
-
-
General Information
EC Number
General Information
Commentary
Organism
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Pseudomonas oleovorans
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Aeribacillus pallidus
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Synechocystis sp. PCC 6803
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Arenibacter latericius
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Arabidopsis thaliana
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Maricaulis maris
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Rhizobium leguminosarum bv. trifolii
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Burkholderia multivorans
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Thalassiosira pseudonana
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Saccharomyces cerevisiae
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Scheffersomyces stipitis
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Methanosarcina mazei
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Bacillus sp. OxB-1
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Pseudomonas entomophila
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Shewanella sediminis
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Microscilla marina
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Janthinobacterium sp. Marseille
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Burkholderia cenocepacia
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Bordetella bronchiseptica
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Geodermatophilus obscurus
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Nocardiopsis dassonvillei ATCC 23218
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Streptomyces albus
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Pantoea sp. AS-PWVM4
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Fodinicurvata sediminis
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Thalassospira lucentensis
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Cellulophaga algicola DSM 14237
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Pseudomonas sp. GM41
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Burkholderia sp. BT03
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Morganella morganii subsp. morganii
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Rubellimicrobium mesophilum
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Tomitella biformata
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Pedobacter jeongneungensis
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Flexithrix dorotheae
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Sediminispirochaeta bajacaliforniensis
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Niabella soli
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Butyrivibrio sp. MC2021
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Dyadobacter alkalitolerans
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Maribacter antarcticus
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Chryseobacterium sp. UNC8MFCol
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Calidithermus chliarophilus
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Sphingobacterium thalpophilum
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Desulfatibacillum aliphaticivorans
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Parabacteroides gordonii
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Pseudonocardia spinosispora
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Stappia stellulata
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Rhodococcus aetherivorans
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Marssonina brunnea
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Blastomyces dermatitidis
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Runella slithyformis
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Rhodococcus rhodochrous
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Starkeya novella
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Comamonas testosteroni
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Burkholderia gladioli
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Agrobacterium rhizogenes
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Mesorhizobium loti
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Oligotropha carboxidovorans
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Achromobacter xylosoxidans
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Rhizoctonia solani
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Variovorax paradoxus
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Bradyrhizobium elkanii
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Methyloversatilis universalis
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Methylibium petroleiphilum
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Sorangium cellulosum
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Paraburkholderia kururiensis
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Pseudomonas syringae pv. syringae
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Teredinibacter turnerae
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Saccharomonospora viridis
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Synechococcus elongatus PCC 6301
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Sphingopyxis alaskensis
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Bradyrhizobium sp. ORS 278
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Shimwellia blattae
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Burkholderia sp. BT03
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Nocardia sp. C-14-1
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Rhizobium leguminosarum bv. viciae 3841
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Danaus plexippus
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Polycyclovorans algicola
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Methylobacterium sp. L2-4
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Bosea sp. 117
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Bradyrhizobium sp. th.b2
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Azospirillum halopraeferens
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Rhizobium sp. JGI 0001019-L19
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Paraburkholderia mimosarum
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Amycolatopsis taiwanensis
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Variovorax sp. P21
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Acidovorax oryzae
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Methylobacterium sp. 88A
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Methylopila sp. 73B
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Xanthobacter sp. 126
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Colletotrichum fioriniae
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Marinomonas ushuaiensis
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Betaproteobacteria bacterium
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Cupriavidus sp. WS
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Methylopila sp. M107
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Serratia sp. M24T3
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Janthinobacterium sp. Marseille
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class.The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Rhizobium leguminosarum
General Information (protein specific)
EC Number
General Information
Commentary
Organism
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Pseudomonas oleovorans
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Aeribacillus pallidus
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Synechocystis sp. PCC 6803
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Arenibacter latericius
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Arabidopsis thaliana
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Maricaulis maris
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Rhizobium leguminosarum bv. trifolii
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Burkholderia multivorans
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Thalassiosira pseudonana
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Saccharomyces cerevisiae
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Scheffersomyces stipitis
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Methanosarcina mazei
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Bacillus sp. OxB-1
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Pseudomonas entomophila
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Shewanella sediminis
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Microscilla marina
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Janthinobacterium sp. Marseille
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Burkholderia cenocepacia
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Bordetella bronchiseptica
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Geodermatophilus obscurus
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Nocardiopsis dassonvillei ATCC 23218
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Streptomyces albus
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Pantoea sp. AS-PWVM4
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Fodinicurvata sediminis
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Thalassospira lucentensis
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Cellulophaga algicola DSM 14237
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Pseudomonas sp. GM41
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Burkholderia sp. BT03
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Morganella morganii subsp. morganii
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Rubellimicrobium mesophilum
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Tomitella biformata
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Pedobacter jeongneungensis
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Flexithrix dorotheae
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Sediminispirochaeta bajacaliforniensis
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Niabella soli
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Butyrivibrio sp. MC2021
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Dyadobacter alkalitolerans
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Maribacter antarcticus
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Chryseobacterium sp. UNC8MFCol
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Calidithermus chliarophilus
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Sphingobacterium thalpophilum
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Desulfatibacillum aliphaticivorans
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Parabacteroides gordonii
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Pseudonocardia spinosispora
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Stappia stellulata
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Rhodococcus aetherivorans
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Marssonina brunnea
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Blastomyces dermatitidis
3.5.5.1
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Runella slithyformis
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Rhodococcus rhodochrous
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Starkeya novella
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Comamonas testosteroni
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Burkholderia gladioli
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Agrobacterium rhizogenes
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Mesorhizobium loti
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Oligotropha carboxidovorans
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Achromobacter xylosoxidans
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Rhizoctonia solani
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Variovorax paradoxus
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Bradyrhizobium elkanii
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Methyloversatilis universalis
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Methylibium petroleiphilum
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Sorangium cellulosum
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Paraburkholderia kururiensis
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Pseudomonas syringae pv. syringae
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Teredinibacter turnerae
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Saccharomonospora viridis
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Synechococcus elongatus PCC 6301
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Sphingopyxis alaskensis
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Bradyrhizobium sp. ORS 278
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Shimwellia blattae
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Burkholderia sp. BT03
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Nocardia sp. C-14-1
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Rhizobium leguminosarum bv. viciae 3841
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Danaus plexippus
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Polycyclovorans algicola
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Methylobacterium sp. L2-4
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Bosea sp. 117
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Bradyrhizobium sp. th.b2
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Azospirillum halopraeferens
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Rhizobium sp. JGI 0001019-L19
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Paraburkholderia mimosarum
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Amycolatopsis taiwanensis
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Variovorax sp. P21
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Acidovorax oryzae
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Methylobacterium sp. 88A
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Methylopila sp. 73B
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Xanthobacter sp. 126
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Colletotrichum fioriniae
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Marinomonas ushuaiensis
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Betaproteobacteria bacterium
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Cupriavidus sp. WS
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Methylopila sp. M107
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Serratia sp. M24T3
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Janthinobacterium sp. Marseille
3.5.5.7
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class.The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
Rhizobium leguminosarum