EC Number | Organism | UniProt | Comment | 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 | Drepanopeziza brunnea | - |
- |
- |
3.5.5.1 | Drepanopeziza brunnea MB_m1 | - |
- |
- |
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 | 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 | Afipia carboxidovorans | - |
- |
- |
3.5.5.7 | Afipia carboxidovorans OM5 | - |
- |
- |
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 | 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 | - |
- |
- |
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 |