AIMC Topic: Computational Biology

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NRGCNMDA: Microbe-Drug Association Prediction Based on Residual Graph Convolutional Networks and Conditional Random Fields.

Interdisciplinary sciences, computational life sciences Jan 7, 2025
The process of discovering new drugs related to microbes through traditional biological methods is lengthy and costly. In response to these issues, a new computational model (NRGCNMDA) is proposed to predict microbe-drug associations. First, Node2vec...
Neural Networks, Computer Computational Biology Listeria monocytogenes Enterococcus faecalis Algorithms Anti-Bacterial Agents Deep Learning
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SProtFP: a machine learning-based method for functional classification of small ORFs in prokaryotes.

NAR genomics and bioinformatics Jan 7, 2025
Small proteins (≤100 amino acids) play important roles across all life forms, ranging from unicellular bacteria to higher organisms. In this study, we have developed SProtFP which is a machine learning-based method for functional annotation of prokar...
Machine Learning Bacteria Computational Biology Gastrointestinal Microbiome Bacterial Proteins Open Reading Frames Molecular Sequence Annotation Humans Antimicrobial Peptides Neural Networks, Computer
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Deep Learning Approaches for the Prediction of Protein Functional Sites.

Molecules (Basel, Switzerland) Jan 7, 2025
Knowing which residues of a protein are important for its function is of paramount importance for understanding the molecular basis of this function and devising ways of modifying it for medical or biotechnological applications. Due to the difficulty...
Sequence Analysis, Protein Databases, Protein Binding Sites Deep Learning Computational Biology Humans Proteins
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Uncovering the epigenetic regulatory clues of PRRT1 in Alzheimer's disease: a strategy integrating multi-omics analysis with explainable machine learning.

Alzheimer's research & therapy Jan 7, 2025
BACKGROUND: Alzheimer's disease (AD) is a complex neurodegenerative disorder with a largely unexplored epigenetic landscape.
Nerve Tissue Proteins Female Epigenomics Alzheimer Disease DNA Methylation Male Humans Computational Biology Gene Expression Profiling Machine Learning Multiomics Epigenesis, Genetic
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Hierarchical Graph Transformer With Contrastive Learning for Gene Regulatory Network Inference.

IEEE journal of biomedical and health informatics Jan 7, 2025
Gene regulatory networks (GRNs) are crucial for understanding gene regulation and cellular processes. Inferring GRNs helps uncover regulatory pathways, shedding light on the regulation and development of cellular processes. With the rise of high-thro...
Gene Regulatory Networks Computational Biology Machine Learning Algorithms Humans Neural Networks, Computer
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MiRS-HF: A Novel Deep Learning Predictor for Cancer Classification and miRNA Expression Patterns.

IEEE journal of biomedical and health informatics Jan 7, 2025
Cancer classification and biomarker identification are crucial for guiding personalized treatment. To make effective use of miRNA associations and expression data, we have developed a deep learning model for cancer classification and biomarker identi...
Humans Gene Expression Profiling Deep Learning Computational Biology Algorithms Biomarkers, Tumor Neoplasms MicroRNAs
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Self-Supervised Contrastive Learning on Attribute and Topology Graphs for Predicting Relationships Among lncRNAs, miRNAs and Diseases.

IEEE journal of biomedical and health informatics Jan 7, 2025
Exploring associations between long non-coding RNAs (lncRNAs), microRNAs (miRNAs) and diseases is crucial for disease prevention, diagnosis and treatment. While determining these relationships experimentally is resource-intensive and time-consuming, ...
RNA, Long Noncoding Supervised Machine Learning Disease Algorithms Humans Computational Biology MicroRNAs
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Deep Drug Synergy Prediction Network Using Modified Triangular Mutation-Based Differential Evolution.

IEEE journal of biomedical and health informatics Jan 7, 2025
Drug combination therapy is crucial in cancer treatment, but accurately predicting drug synergy remains a challenge due to the complexity of drug combinations. Machine learning and deep learning models have shown promise in drug combination predictio...
Mutation Drug Synergism Deep Learning Computational Biology Humans Algorithms Neoplasms
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SequenceCraft: machine learning-based resource for exploratory analysis of RNA-cleaving deoxyribozymes.

BMC bioinformatics Jan 6, 2025
BACKGROUND: Deoxyribozymes or DNAzymes represent artificial short DNA sequences bearing many catalytic properties. In particular, DNAzymes able to cleave RNA sequences have a huge potential in gene therapy and sequence-specific analytic detection of ...
Computational Biology RNA DNA, Catalytic Machine Learning
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Integrating bioinformatics and machine learning to identify AhR-related gene signatures for prognosis and tumor microenvironment modulation in melanoma.

Frontiers in immunology Jan 6, 2025
BACKGROUND: The Aryl Hydrocarbon Receptor (AhR) pathway significantly influences immune cell regulation, impacting the effectiveness of immunotherapy and patient outcomes in melanoma. However, the specific downstream targets and mechanisms by which A...
Gene Expression Regulation, Neoplastic Prognosis Tumor Microenvironment Mice Animals Melanoma Biomarkers, Tumor Databases, Genetic Computational Biology Skin Neoplasms Humans Gene Expression Profiling Basic Helix-Loop-Helix Transcription Factors Lymphocytes, Tumor-Infiltrating Machine Learning Transcriptome Receptors, Aryl Hydrocarbon
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