AIMC Topic: Computational Biology

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Developing a Panel of Shared Susceptibility Genes as Diagnostic Biomarkers for chronic obstructive pulmonary disease and Heart Failure.

Computers in biology and medicine
AIM: Chronic obstructive pulmonary disease (COPD) and heart failure (HF) are closely intertwined comorbidities that present significant clinical challenges due to the poorly understood pathophysiological mechanisms driving their coexistence. In this ...

EnsemPred-ACP: Combining machine and deep learning to improve anticancer peptide prediction.

Computers in biology and medicine
Anticancer peptide (ACP) has emerged as potent therapeutic agents owing to its ability to selectively target cancer cells while minimising toxicity to healthy cells. However, the accurate computational prediction of ACP remains challenging because of...

Combination of machine learning and protein‑protein interaction network established one ATM‑DPP4‑TXN ferroptotic diagnostic model with experimental validation.

Molecular medicine reports
Ferroptosis and lethal sepsis are interlinked, although this association remains largely unknown to clinical panels. Sepsis is characterized by dysfunction of the inflammatory microenvironment. Most septic biomarkers lack independent validation, and ...

Identification of hub genes involved in the pathogenesis of diabetic nephropathy: A multi-omics study integrating machine learning, mendelian randomization and mediation analysis.

Diabetes, obesity & metabolism
BACKGROUND: Diabetic nephropathy (DN), affecting 30%-40% of diabetic patients, is the leading cause of end-stage renal disease worldwide. This study aims to identify diagnostic biomarkers and explore potential gene-metabolite interactions in DN patho...

DCBLSTM-Deep Convolutional Bidirectional Long Short-Term Memory neural network for Q8 secondary protein structure prediction.

Computers in biology and medicine
Protein secondary structure prediction involves determining a protein's secondary structure from its primary amino acid sequence, serving as a critical step toward tertiary structure prediction. This, in turn, is essential for applications in drug de...

BindUP-Alpha: A Webserver for Predicting DNA-and RNA-binding Proteins based on Experimental and Computational Structural Models☆.

Journal of molecular biology
Structural data provides important information on the proteins' function. Recent development of advanced machine learning and artificial intelligence tools, such as AlphaFold, have led to an explosion of predicted protein structures. However, many of...

pLMMoRF: A Web Server That Accurately Predicts Membrane-interacting Molecular Recognition Features by Employing a Protein Language Model.

Journal of molecular biology
Interactions between proteins and lipids are crucial for numerous cellular processes. Some of the lipid interacting segments in protein sequences are intrinsically disordered regions (IDRs), which may gain secondary structures upon binding. We collec...

Predicting CircRNA-Disease Associations Based on Heterogeneous Graph Neural Network and Knowledge Graph Attribute Mining Attention.

Interdisciplinary sciences, computational life sciences
The exploration of associations between circular RNAs (circRNAs) and diseases contributes to a deeper understanding of the pathogenesis of diseases. Many computational methods have been proposed for circRNA-disease associations identification. Howeve...

Identification of Npas4 as a biomarker for CICI by transcriptomics combined with bioinformatics and machine learning approaches.

Experimental neurology
Chemotherapy is one of the most successful strategies for treating cancer. Unfortunately, up to 70 % of cancer survivors develop cognitive impairment during or after chemotherapy, which severely affects their quality of life. We first established a m...

Computational strategies in systems-level stress response data analysis.

Biological chemistry
Stress responses in biological systems arise from complex, dynamic interactions among genes, proteins, and metabolites. A thorough understanding of these responses requires examining not only changes in individual molecular components but also their ...