Supplementary table of the article Machine Learning Study of Metabolic Networks vs ChEMBL Data of Antibacterial Compounds [Dataset]
22 pages. -- Table S01. Statistics for multiple types of biological activity parameters in ChEMBL dataset. -- Table S02. Details of the metabolic networks of >40 organisms. -- Table S03. Average values of fk for the metabolic networks of >40 organisms. -- Table S04. Conditions includes in ChEM...
| Autores: | , , , , , |
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| Tipo de recurso: | conjunto de datos |
| Fecha de publicación: | 2022 |
| País: | España |
| Institución: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:digital.csic.es:10261/331472 |
| Acceso en línea: | http://hdl.handle.net/10261/331472 |
| Access Level: | acceso abierto |
| Palabra clave: | Receiver operating characteristic Predict antibacterial compounds Nonlinear models regarding Model also presented K nearest neighbors Good statistical parameters Antibacterial drug research Understanding metabolic network Linear discriminant analysis Multiple bacteria species Lda model presented Ifptml nonlinear models Machine learning study Machine learning 000 cases Metabolic status Metabolic networks Linear index Ifptml linear Validation subsets Validation series Training subset Training sets Reducing time Random forest Perturbation theory Information fusion Huge dataset Following results Chembl database Chembl data Best results Bacterial death Antibiotic resistance |
| Sumario: | 22 pages. -- Table S01. Statistics for multiple types of biological activity parameters in ChEMBL dataset. -- Table S02. Details of the metabolic networks of >40 organisms. -- Table S03. Average values of fk for the metabolic networks of >40 organisms. -- Table S04. Conditions includes in ChEMBL Dataset of Antibacterial Drugs vs MRN analysis. -- Table S05. Linear index based on atoms descriptors included in the model. |
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