AIMC Topic: Computational Biology

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Machine learning approach to gene essentiality prediction: a review.

Briefings in bioinformatics Sep 2, 2021
UNLABELLED: Essential genes are critical for the growth and survival of any organism. The machine learning approach complements the experimental methods to minimize the resources required for essentiality assays. Previous studies revealed the need to...
Support Vector Machine Animals Caenorhabditis elegans Genes, Essential Gene Ontology Gene Regulatory Networks Computational Biology Humans Machine Learning Algorithms
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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...
Glioblastoma Central Nervous System Diseases Brain Neoplasms Disease Progression Computational Biology Machine Learning Algorithms Humans
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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...
Humans Proteins Pharmaceutical Preparations Machine Learning Computational Biology Models, Theoretical Algorithms Drug Development Reproducibility of Results Support Vector Machine Protein Binding
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Venn diagrams in bioinformatics.

Briefings in bioinformatics Sep 2, 2021
Venn diagrams are widely used tools for graphical depiction of the unions, intersections and distinctions among multiple datasets, and a large number of programs have been developed to generate Venn diagrams for applications in various research areas...
Gene Regulatory Networks Computational Biology Models, Theoretical Computer Graphics Databases, Genetic Gene Ontology Software Animals Gene Expression Profiling Humans
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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 ...
Databases, Protein Humans Models, Molecular Molecular Structure Quantitative Structure-Activity Relationship Binding, Competitive Pharmaceutical Preparations Drug Discovery Machine Learning Algorithms Ligands Protein Binding Drug Design Computational Biology Proteins
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SAResNet: self-attention residual network for predicting DNA-protein binding.

Briefings in bioinformatics Sep 2, 2021
Knowledge of the specificity of DNA-protein binding is crucial for understanding the mechanisms of gene expression, regulation and gene therapy. In recent years, deep-learning-based methods for predicting DNA-protein binding from sequence data have a...
Computational Biology DNA Internet DNA-Binding Proteins Binding Sites Reproducibility of Results Deep Learning Protein Binding Humans Neural Networks, Computer Algorithms
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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...
Computational Biology Protein Processing, Post-Translational Proteins Databases, Protein Machine Learning Algorithms Reproducibility of Results Models, Biological Humans Neural Networks, Computer
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DeepDRK: a deep learning framework for drug repurposing through kernel-based multi-omics integration.

Briefings in bioinformatics Sep 2, 2021
Recent pharmacogenomic studies that generate sequencing data coupled with pharmacological characteristics for patient-derived cancer cell lines led to large amounts of multi-omics data for precision cancer medicine. Among various obstacles hindering ...
Precision Medicine Prognosis Neoplasms Antineoplastic Agents Humans Software Cell Line, Tumor Computational Biology Drug Repositioning Datasets as Topic Transcriptome Deep Learning Pharmacogenetics
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Deep embedded clustering with multiple objectives on scRNA-seq data.

Briefings in bioinformatics Sep 2, 2021
In recent years, single-cell RNA sequencing (scRNA-seq) technologies have been widely adopted to interrogate gene expression of individual cells; it brings opportunities to understand the underlying processes in a high-throughput manner. Deep embedde...
Mutation Gene Ontology Computational Biology Models, Genetic Cluster Analysis Humans Neural Networks, Computer Algorithms Single-Cell Analysis RNA-Seq Reproducibility of Results Gene Expression Profiling
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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 Computational Biology Evolution, Molecular Databases, Protein Datasets as Topic Machine Learning Software Peptides
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