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Literature summary extracted from

  • Sharma, N.; Verma, R.; Savitri, R.; Bhalla, T.C.
    Classifying nitrilases as aliphatic and aromatic using machine learning technique (2018), 3 Biotech, 8, 68 .
    View publication on PubMedView publication on EuropePMC

Organism

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

EC Number General Information Comment 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 Drepanopeziza 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 Afipia 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