AIMC Topic: Computational Biology

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Identification and validation of inflammatory response genes linking chronic kidney disease with coronary artery disease based on bioinformatics and machine learning.

Scientific reports
Coronary artery disease (CAD) commonly occurs and elevates the risk of cardiovascular events and mortality in chronic kidney disease (CKD) patients. The underlying pathogenesis of CKD-related CAD is believed to be closely linked to inflammatory respo...

m6A-SPP: Identification of RNA N6-methyladenosine modification sites through multi-source biological features and a hybrid deep learning architecture.

International journal of biological macromolecules
The N6-methyladenosine(m6A) modification plays crucial regulatory roles in various biological processes including gene expression regulation, RNA stability, splicing, and translation. Accurate prediction of m6A modification sites is essential for und...

Computational methods for modeling protein-protein interactions in the AI era: Current status and future directions.

Drug discovery today
The modeling of protein-protein interactions (PPIs) has been revolutionized by artificial intelligence, with deep learning and end-to-end frameworks such as AlphaFold and its derivatives now dominating the field. This review surveys the current compu...

Predicted and Explained: Transforming drug discovery with AI for high-precision receptor-ligand interaction modeling and binding analysis.

Computers in biology and medicine
The pharmaceutical industry faces persistent challenges in developing effective treatments for complex diseases, creating an urgent need for innovative approaches to accelerate drug discovery. A pivotal factor in this process is the accurate predicti...

Autophagy-related gene SQSTM1 predicts the prognosis of hepatocellular carcinoma.

Computers in biology and medicine
BACKGROUND: The relationship between autophagy and the progression of hepatocellular carcinoma (HCC) is notably substantial, yet the underlying mechanisms remain incompletely elucidated. Our objective is to construct a predictive model, thereby provi...

MiRAGE-DTI: A novel approach for drug-target interaction prediction by integrating drug and target similarity metrics.

Computers in biology and medicine
MOTIVATION: Accurately predicting drug-target interactions (DTIs) is critical for accelerating drug discovery, repositioning, and development. Traditional experimental methods are often expensive and time-consuming, emphasizing the need for efficient...

KDELR3 and YOD1 proteins as critical endoplasmic reticulum stress mediators and potential therapeutic targets in diabetic foot ulcers: An integrated bioinformatics analysis.

International journal of biological macromolecules
BACKGROUND: Diabetic foot ulcers (DFU) represent one of the most severe complications of diabetes mellitus and are closely associated with persistent hyperglycemia. Endoplasmic reticulum stress response proteins play critical roles in the development...

Identification and prioritization of disease candidate genes using biomedical named entity recognition and random forest classification.

Computers in biology and medicine
BACKGROUND AND OBJECTIVE: The elucidation of candidate genes is fundamental to comprehending intricate diseases, vital for early diagnosis, personalized treatment, and drug discovery. Traditional Disease Gene Identification methods encounter limitati...

Exploring the toxicological impact of bisphenol a exposure on psoriasis through network toxicology, machine learning, and multi-dimensional bioinformatics analysis.

Journal of environmental management
Psoriasis is a common immune - mediated skin disease, the pathogenesis of which is not completely elucidated. Environmental factors are key to its onset and progression. Bisphenol A (BPA) is a ubiquitous environmental pollutant that endangers human h...