AIMC Topic: Machine Learning

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Bioinformatics and machine learning approach identifies potential drug targets and pathways in COVID-19.

Briefings in bioinformatics Sep 2, 2021
Current coronavirus disease-2019 (COVID-19) pandemic has caused massive loss of lives. Clinical trials of vaccines and drugs are currently being conducted around the world; however, till now no effective drug is available for COVID-19. Identification...
Central Nervous System Diseases Brain Neoplasms Humans Disease Progression Computational Biology Machine Learning Algorithms Glioblastoma
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Machine learning revealed stemness features and a novel stemness-based classification with appealing implications in discriminating the prognosis, immunotherapy and temozolomide responses of 906 glioblastoma patients.

Briefings in bioinformatics Sep 2, 2021
Glioblastoma (GBM) is the most malignant and lethal intracranial tumor, with extremely limited treatment options. Immunotherapy has been widely studied in GBM, but none can significantly prolong the overall survival (OS) of patients without selection...
Temozolomide Antineoplastic Agents, Alkylating Atlases as Topic Humans Machine Learning Prognosis Tumor Microenvironment Middle Aged Gene Expression Regulation, Neoplastic Female Brain Neoplasms Gene Expression Profiling Treatment Outcome Male Aged Transcriptome Neoplastic Stem Cells Adult Immunotherapy Glioblastoma Patient Selection Programmed Cell Death 1 Receptor Survival Analysis
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Integrating multi-scale neighbouring topologies and cross-modal similarities for drug-protein interaction prediction.

Briefings in bioinformatics Sep 2, 2021
MOTIVATION: Identifying the proteins that interact with drugs can reduce the cost and time of drug development. Existing computerized methods focus on integrating drug-related and protein-related data from multiple sources to predict candidate drug-t...
Protein Binding Computational Biology Models, Theoretical Drug Development Support Vector Machine Proteins Pharmaceutical Preparations Humans Machine Learning Algorithms Reproducibility of Results
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SMMPPI: a machine learning-based approach for prediction of modulators of protein-protein interactions and its application for identification of novel inhibitors for RBD:hACE2 interactions in SARS-CoV-2.

Briefings in bioinformatics Sep 2, 2021
Small molecule modulators of protein-protein interactions (PPIs) are being pursued as novel anticancer, antiviral and antimicrobial drug candidates. We have utilized a large data set of experimentally validated PPI modulators and developed machine le...
Protein Interaction Maps Humans Machine Learning Proteins SARS-CoV-2 Angiotensin-Converting Enzyme 2
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Persistent spectral hypergraph based machine learning (PSH-ML) for protein-ligand binding affinity prediction.

Briefings in bioinformatics Sep 2, 2021
Molecular descriptors are essential to not only quantitative structure activity/property relationship (QSAR/QSPR) models, but also machine learning based chemical and biological data analysis. In this paper, we propose persistent spectral hypergraph ...
Pharmaceutical Preparations Ligands Quantitative Structure-Activity Relationship Binding, Competitive Databases, Protein Models, Molecular Molecular Structure Machine Learning Algorithms Drug Design Humans Drug Discovery Protein Binding Computational Biology Proteins
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A comprehensive review of the imbalance classification of protein post-translational modifications.

Briefings in bioinformatics Sep 2, 2021
Post-translational modifications (PTMs) play significant roles in regulating protein structure, activity and function, and they are closely involved in various pathologies. Therefore, the identification of associated PTMs is the foundation of in-dept...
Proteins Databases, Protein Models, Biological Humans Neural Networks, Computer Machine Learning Algorithms Reproducibility of Results Computational Biology Protein Processing, Post-Translational
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ATSE: a peptide toxicity predictor by exploiting structural and evolutionary information based on graph neural network and attention mechanism.

Briefings in bioinformatics Sep 2, 2021
MOTIVATION: Peptides have recently emerged as promising therapeutic agents against various diseases. For both research and safety regulation purposes, it is of high importance to develop computational methods to accurately predict the potential toxic...
Humans Neural Networks, Computer Machine Learning Software Databases, Protein Datasets as Topic Peptides Computational Biology Evolution, Molecular
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NMCMDA: neural multicategory MiRNA-disease association prediction.

Briefings in bioinformatics Sep 2, 2021
MOTIVATION: There is growing evidence showing that the dysregulations of miRNAs cause diseases through various kinds of the underlying mechanism. Thus, predicting the multiple-category associations between microRNAs (miRNAs) and diseases plays an imp...
Humans Neural Networks, Computer MicroRNAs Feasibility Studies Data Accuracy Lung Neoplasms Genetic Predisposition to Disease Computational Biology Databases, Genetic Machine Learning Female Breast Neoplasms
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Comprehensive assessment of machine learning-based methods for predicting antimicrobial peptides.

Briefings in bioinformatics Sep 2, 2021
Antimicrobial peptides (AMPs) are a unique and diverse group of molecules that play a crucial role in a myriad of biological processes and cellular functions. AMP-related studies have become increasingly popular in recent years due to antimicrobial r...
Fungi Viruses Pore Forming Cytotoxic Proteins Bacteria Computational Biology Support Vector Machine Machine Learning Algorithms Databases, Factual Biofilms
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PySmash: Python package and individual executable program for representative substructure generation and application.

Briefings in bioinformatics Sep 2, 2021
BACKGROUND: Substructure screening is widely applied to evaluate the molecular potency and ADMET properties of compounds in drug discovery pipelines, and it can also be used to interpret QSAR models for the design of new compounds with desirable phys...
Humans Computational Biology Drug Screening Assays, Antitumor Carcinogens Carcinogenicity Tests Machine Learning Software Drug Discovery
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