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Literature summary for 3.5.5.1 extracted from
- Classifying nitrilases as aliphatic and aromatic using machine learning technique (2018), 3 Biotech, 8, 68 .
Organism
| Organism | UniProt | Comment | Textmining |
|---|---|---|---|
| Aeribacillus pallidus | - |
- |
- |
| Arabidopsis thaliana | P46010 | - |
- |
| Arenibacter latericius | - |
- |
- |
| Bacillus sp. OxB-1 | P82605 | - |
- |
| Blastomyces dermatitidis | - |
- |
- |
| Blastomyces dermatitidis ER-3 | - |
- |
- |
| Bordetella bronchiseptica | A0A0H3LIT0 | - |
- |
| Burkholderia cenocepacia | B4EE44 | - |
- |
| Burkholderia multivorans | B9BCZ1 | - |
- |
| Burkholderia multivorans CGD1 | B9BCZ1 | - |
- |
| Burkholderia sp. BT03 | - |
- |
- |
| Butyrivibrio sp. MC2021 | - |
- |
- |
| Calidithermus chliarophilus | - |
- |
- |
| Cellulophaga algicola DSM 14237 | - |
- |
- |
| Chryseobacterium sp. UNC8MFCol | - |
- |
- |
| Desulfatibacillum aliphaticivorans | - |
- |
- |
| Drepanopeziza brunnea | - |
- |
- |
| Drepanopeziza brunnea MB_m1 | - |
- |
- |
| Dyadobacter alkalitolerans | - |
- |
- |
| Flexithrix dorotheae | - |
- |
- |
| Fodinicurvata sediminis | - |
- |
- |
| Geodermatophilus obscurus | D2SGH7 | - |
- |
| Geodermatophilus obscurus ATCC 25078 | D2SGH7 | - |
- |
| Janthinobacterium sp. Marseille | A6T0X3 | - |
- |
| Maribacter antarcticus | - |
- |
- |
| Maricaulis maris | - |
- |
- |
| Maricaulis maris MCS10 | - |
- |
- |
| Methanosarcina mazei | Q8PXI9 | - |
- |
| Methanosarcina mazei BAA-159 | Q8PXI9 | - |
- |
| Microscilla marina | A1ZD79 | - |
- |
| Microscilla marina ATCC 23134 | A1ZD79 | - |
- |
| Morganella morganii subsp. morganii | - |
- |
- |
| Morganella morganii subsp. morganii KT | - |
- |
- |
| Niabella soli | - |
- |
- |
| Niabella soli DSM 19437 | - |
- |
- |
| Nocardiopsis dassonvillei ATCC 23218 | D7B8P3 | - |
- |
| Pantoea sp. AS-PWVM4 | - |
- |
- |
| Parabacteroides gordonii | - |
- |
- |
| Pedobacter jeongneungensis | - |
- |
- |
| Pseudomonas entomophila | Q1I7X1 | - |
- |
| Pseudomonas entomophila L48 | Q1I7X1 | - |
- |
| Pseudomonas oleovorans | - |
- |
- |
| Pseudomonas oleovorans CECT:5344 | - |
- |
- |
| Pseudomonas sp. GM41 | - |
- |
- |
| Pseudonocardia spinosispora | - |
- |
- |
| Rhizobium leguminosarum bv. trifolii | - |
- |
- |
| Rhizobium leguminosarum bv. trifolii WSM1325 | - |
- |
- |
| Rhodococcus aetherivorans | - |
- |
- |
| Rubellimicrobium mesophilum | - |
- |
- |
| Rubellimicrobium mesophilum DSM 19309 | - |
- |
- |
| Runella slithyformis | - |
- |
- |
| Saccharomyces cerevisiae | B3LU11 | - |
- |
| Saccharomyces cerevisiae RM11-1a | B3LU11 | - |
- |
| Scheffersomyces stipitis | A3LXL4 | - |
- |
| Scheffersomyces stipitis ATCC 58785 | A3LXL4 | - |
- |
| Sediminispirochaeta bajacaliforniensis | - |
- |
- |
| Shewanella sediminis | A8FQL4 | - |
- |
| Shewanella sediminis HAW-EB3 | A8FQL4 | - |
- |
| Sphingobacterium thalpophilum | - |
- |
- |
| Stappia stellulata | - |
- |
- |
| Streptomyces albus | D6B7V7 | - |
- |
| Streptomyces albus J1074 | D6B7V7 | - |
- |
| Synechocystis sp. PCC 6803 | - |
- |
- |
| Thalassiosira pseudonana | B8C1M9 | - |
- |
| Thalassospira lucentensis | - |
- |
- |
| Tomitella biformata | - |
- |
- |
General Information
| General Information | Comment | Organism |
|---|---|---|
| metabolism | use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning 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 |
| metabolism | use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning 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 |
| metabolism | use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning 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 |
| metabolism | use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning 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 |
| metabolism | use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning 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 |
| metabolism | use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning 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 |
| metabolism | use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning 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 |
| metabolism | use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning 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 |
| metabolism | use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning 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 |
| metabolism | use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning 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 |
| metabolism | use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning 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 |
| metabolism | use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning 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 |
| metabolism | use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning 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 |
| metabolism | use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning 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 |
| metabolism | use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning 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 |
| metabolism | use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning 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 |
| metabolism | use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning 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 |
| metabolism | use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning 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 |
| metabolism | use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning 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 |
| metabolism | use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning 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 |
| metabolism | use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning 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 |
| metabolism | use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning 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 |
| metabolism | use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning 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 |
| metabolism | use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning 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 |
| metabolism | use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning 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 |
| metabolism | use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning 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 |
| metabolism | use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning 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 |
| metabolism | use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning 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 |
| metabolism | use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning 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 |
| metabolism | use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning 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 |
| metabolism | use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning 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 |
| metabolism | use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning 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 |
| metabolism | use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning 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 |
| metabolism | use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning 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 |
| metabolism | use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning 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 |
| metabolism | use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning 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 |
| metabolism | use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning 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 |
| metabolism | use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning 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 |
| metabolism | use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning 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 |
| metabolism | use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning 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 |
| metabolism | use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning 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 |
| metabolism | use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning 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 |
| metabolism | use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning 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 |
| metabolism | use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning 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 |
| metabolism | use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning 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 |
| metabolism | use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning 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 |
| metabolism | use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning 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 |
| metabolism | use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning 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 |
| metabolism | use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning 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 |
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