Understanding the vast noncoding cancer genome requires cutting-edge, high-resolution, and accessible strategies. Artificial intelligence is revolutionizing cancer research, enabling advanced models to analyze genome regulation. This review examines ...
MOTIVATION: The growing complexity of clinical cancer research has fueled a surge in demand for automated bioinformatics tools capable of integrating clinical and genomic data to accelerate discovery efforts.
Protein science : a publication of the Protein Society
Jul 1, 2025
Accurate prediction of protein function is fundamental to understanding biological processes, with computational methods becoming increasingly essential as experimental methods struggle to keep pace with the rate of newly discovered proteins. Despite...
MOTIVATION: Clustering cells into subpopulations is one of the most crucial tasks in single-cell RNA sequencing (scRNA-seq) data analysis, which provides support for biological research at cellular level. With the development of graph neural networks...
Lactate metabolism is implicated in myocardial infarction (MI), yet the underlying mechanisms are not fully understood. Identifying lactate metabolism-related genes (LMRGs) could uncover new diagnostic and therapeutic targets for MI. We conducted a b...
Background: Sepsis-associated acute kidney injury (SA-AKI) is a life-threatening complication with mortality rates exceeding 50%, yet its molecular drivers remain poorly defined. Dysregulated coagulation is increasingly implicated in SA-AKI pathogene...
BACKGROUND: The molecular and genetic mechanisms underlying vascular calcification remain unclear. This study aimed to determine the differences in calcification marker-related gene expression in macrophages.
With the rise of small interfering RNA (siRNA) as a therapeutic tool, effective siRNA design is crucial. Current methods often emphasize sequence-related features, overlooking structural information. To address this, we introduce ENsiRNA, a multimoda...
Peptide toxicity prediction holds significant importance in drug development and biotechnology, as accurately identifying toxic peptide sequences is crucial for designing safer peptide-based drugs. This study proposes a deep learning-based model for ...
Single-cell RNA-sequencing (scRNA-seq) coupled with robust computational analysis facilitates the characterization of phenotypic heterogeneity within tumors. Current scRNA-seq analysis pipelines are capable of identifying a myriad of malignant and no...
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.