gemGAT demonstrates superior performance over existing methods by efficiently capturing nonlinear gene coexpressions, underscores gemGAT’s potential to significantly contribute to precision medicine, showcasing its utility in advancing the understanding of gene expression in less accessible tissues.
Shiyu Wang, Tian-Xing Hu, Meng-Yu He et al.· Briefings in Bioinformatics· 0 citations
MSMICA applies multiple pieces of evidence to substantially improve metabolite identification coverage and accuracy for known metabolites and enables greater metabolite identifications, expanded metabolic pathway coverage, and data harmonization.
Jia-Da Zhan, J. Weinberg, William J. Crandall et al.· bioRxiv· 0 citations
LLMBDC (Large Language Model for Biological Domains Oriented Clustering of Gene Ontology) provides a scalable, reproducible, and interpretable route to context-aware, system-level interpretation of GO enrichment results while preserving biological specificity.
Xi-Ming Ran, Jie Xu, Peng Jin et al.· 0 citations
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