AIMC Topic: Computational Biology

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EnsembleSE: identification of super-enhancers based on ensemble learning.

Briefings in functional genomics
Super-enhancers (SEs) are typically located in the regulatory regions of genes, driving high-level gene expression. Identifying SEs is crucial for a deeper understanding of gene regulatory networks, disease mechanisms, and the development and physiol...

A comprehensive review of deep learning-based approaches for drug-drug interaction prediction.

Briefings in functional genomics
Deep learning models have made significant progress in the biomedical field, particularly in the prediction of drug-drug interactions (DDIs). DDIs are pharmacodynamic reactions between two or more drugs in the body, which may lead to adverse effects ...

SCREEN: A Graph-based Contrastive Learning Tool to Infer Catalytic Residues and Assess Enzyme Mutations.

Genomics, proteomics & bioinformatics
The accurate identification of catalytic residues contributes to our understanding of enzyme functions in biological processes and pathways. The increasing number of protein sequences necessitates computational tools for the automated prediction of c...

EnrichDO: a global weighted model for Disease Ontology enrichment analysis.

GigaScience
BACKGROUND: Disease Ontology (DO) has been widely studied in biomedical research and clinical practice to describe the roles of genes. DO enrichment analysis is an effective means to discover associations between genes and diseases. Compared to hundr...

Machine learning-driven identification of exosome- related biomarkers in head and neck squamous cell carcinoma.

Frontiers in immunology
BACKGROUND: Head and neck squamous cell carcinoma (HNSCC) is a common cancer associated with elevated mortality rates. Exosomes, diminutive extracellular vesicles, significantly contribute to tumour development, immunological evasion, and treatment r...

Construction of a prognostic model for endometrial cancer related to programmed cell death using WGCNA and machine learning algorithms.

Frontiers in immunology
BACKGROUND: Programmed cell death (PCD) refers to a regulated and active process of cellular demise, initiated by specific biological signals. PCD plays a crucial role in the development, progression, and drug resistance of uterine corpus endometrial...

Identifying Common Diagnostic Biomarkers and Therapeutic Targets between COPD and Sepsis: A Bioinformatics and Machine Learning Approach.

International journal of chronic obstructive pulmonary disease
BACKGROUND: Evidence suggests a bidirectional association between chronic obstructive pulmonary disease (COPD) and sepsis, but the underlying mechanisms remain unclear. This study aimed to explore shared diagnostic genes, potential mechanisms, and th...

Bioinformatics meets machine learning: identifying circulating biomarkers for vitiligo across blood and tissues.

Frontiers in immunology
BACKGROUND: Vitiligo is a skin disorder characterized by the progressive loss of pigmentation in the skin and mucous membranes. The exact aetiology and pathogenesis of vitiligo remain incompletely understood.

Identification of hub gene for the pathogenic mechanism and diagnosis of MASLD by enhanced bioinformatics analysis and machine learning.

PloS one
Metabolic dysfunction-associated steatotic liver disease (MASLD) is a heterogeneous disease caused by multiple etiologies. It is characterized by excessive fat accumulation in the liver. Without intervention, MASLD can progress from steatosis to meta...

Identification of biomarkers associated with inflammatory response in Parkinson's disease by bioinformatics and machine learning.

PloS one
Parkinson's disease (PD) is a common and debilitating neurodegenerative disorder. The inflammatory response is essential in the pathogenesis and progression of PD. The goal of this study is to combine bioinformatics and machine learning to screen for...