| scholarly article | Q13442814 |
| P8978 | DBLP publication ID | journals/jcheminf/DongYZLLLCC18 |
| P6179 | Dimensions Publication ID | 1101630073 |
| P356 | DOI | 10.1186/S13321-018-0270-2 |
| P932 | PMC publication ID | 5861255 |
| P698 | PubMed publication ID | 29556758 |
| P50 | author | Dong-Sheng Cao | Q56247792 |
| Feijun Luo | Q56798427 | ||
| Jie Dong | Q87725529 | ||
| Ai-Ping Lu | Q116678166 | ||
| P2093 | author name string | Lin Zhang | |
| Qinlu Lin | |||
| Alex F Chen | |||
| Zhi-Jiang Yao | |||
| P2860 | cites work | Bioclipse: an open source workbench for chemo- and bioinformatics | Q21093640 |
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| propy: a tool to generate various modes of Chou's PseAAC. | Q34328788 | ||
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| QuBiLS-MIDAS: a parallel free-software for molecular descriptors computation based on multilinear algebraic maps | Q35180116 | ||
| repDNA: a Python package to generate various modes of feature vectors for DNA sequences by incorporating user-defined physicochemical properties and sequence-order effects | Q35518468 | ||
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| Large-scale prediction of human kinase-inhibitor interactions using protein sequences and molecular topological structures. | Q47871915 | ||
| P275 | copyright license | Creative Commons Attribution 4.0 International | Q20007257 |
| P6216 | copyright status | copyrighted | Q50423863 |
| P4510 | describes a project that uses | Python | Q28865 |
| P433 | issue | 1 | |
| P921 | main subject | Python | Q28865 |
| Python package | Q29642950 | ||
| P6104 | maintained by WikiProject | WikiProject Software | Q15659621 |
| P304 | page(s) | 16 | |
| P577 | publication date | 2018-03-20 | |
| P1433 | published in | Journal of Cheminformatics | Q6294930 |
| P1476 | title | PyBioMed: a python library for various molecular representations of chemicals, proteins and DNAs and their interactions | |
| P478 | volume | 10 |
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| Q92304604 | BioMedR: an R/CRAN package for integrated data analysis pipeline in biomedical study |
| Q112609285 | CardioTox net: a robust predictor for hERG channel blockade based on deep learning meta-feature ensembles |
| Q102131916 | ChemFLuo: a web-server for structure analysis and identification of fluorescent compounds |
| Q89545445 | DeepHIT: a deep learning framework for prediction of hERG-induced cardiotoxicity |
| Q89715684 | Distributed and multicore QuBiLS-MIDAS software v2.0: Computing chiral, fuzzy, weighted and truncated geometrical molecular descriptors based on tensor algebra |
| Q103760836 | ENNAACT is a novel tool which employs neural networks for anticancer activity classification for therapeutic peptides |
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| Q64105393 | Identification of a Novel Bcl-2 Inhibitor by Ligand-Based Screening and Investigation of Its Anti-cancer Effect on Human Breast Cancer Cells |
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| Q113329343 | MathFeature: feature extraction package for DNA, RNA and protein sequences based on mathematical descriptors |
| Q90997556 | MuLiMs-MCoMPAs: A Novel Multiplatform Framework to Compute Tensor Algebra-Based Three-Dimensional Protein Descriptors |
| Q128643865 | Multistep virtual screening for rapid identification of G Protein-Coupled Receptors Kinase 2 inhibitors for heart failure treatment |
| Q112698936 | PDAUG: a Galaxy based toolset for peptide library analysis, visualization, and machine learning modeling |
| Q112611051 | Predicting aptamer sequences that interact with target proteins using an aptamer-protein interaction classifier and a Monte Carlo tree search approach |
| Q112724393 | Predicting cell-penetrating peptides using machine learning algorithms and navigating in their chemical space |
| Q90177361 | Predicting the impacts of mutations on protein-ligand binding affinity based on molecular dynamics simulations and machine learning methods |
| Q91307555 | Prediction of drug-target interaction based on protein features using undersampling and feature selection techniques with boosting |
| Q114179545 | Predictive and explanatory themes of NOAEL through a systematic comparison of different machine learning methods and descriptors |
| Q113292718 | ProPythia: A Python package for protein classification based on machine and deep learning |
| Q90587163 | Structural Analysis and Identification of False Positive Hits in Luciferase-Based Assays |
| Q104614143 | Systems Approach to Pathogenic Mechanism of Type 2 Diabetes and Drug Discovery Design Based on Deep Learning and Drug Design Specifications |
| Q114854688 | Systems Drug Discovery for Diffuse Large B Cell Lymphoma Based on Pathogenic Molecular Mechanism via Big Data Mining and Deep Learning Method |
| Q128009350 | iDTi-CSsmoteB: Identification of Drug–Target Interaction Based on Drug Chemical Structure and Protein Sequence Using XGBoost With Over-Sampling Technique SMOTE |
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