Predicting molecular processes using deep learning is a promising approach to provide biological insights for non-coding single nucleotide polymorphisms identified in genome-wide association studies. However, most deep learning methods rely on superv...
Ischemic stroke (IS) is a leading cause of adult disability that can severely compromise the quality of life for patients. Accurately predicting the IS functional outcome is crucial for precise risk stratification and effective therapeutic interventi...
Machine learning (ML) has become increasingly popular in almost all scientific disciplines, including human genetics. Owing to challenges related to sample collection and precise phenotyping, ML-assisted genome-wide association study (GWAS), which us...
Transcriptome-wide association studies (TWAS) aim to uncover genotype-phenotype relationships through a two-stage procedure: predicting gene expression from genotypes using an expression quantitative trait locus (eQTL) data set, then testing the pred...
Cattle breed identification is crucial for livestock research and sustainable food systems, and advances in genomics and artificial intelligence present new opportunities to address these challenges. This study investigates the identification of the ...
Large-scale, multi-ethnic whole-genome sequencing (WGS) studies, such as the National Human Genome Research Institute Genome Sequencing Program's Centers for Common Disease Genomics (CCDG), play an important role in increasing diversity for genetic r...
Understanding how genetic variants affect the epigenome is key to interpreting GWAS, yet profiling these effects across the non-coding genome remains challenging due to experimental scalability. This necessitates accurate computational models. Existi...
BACKGROUND AND AIM: Carbonated sugar-sweetened beverages (CSSB) intake has been increasingly linked to metabolic diseases. To investigate the association between CSSB intake and metabolic syndrome (MetS) risk, and the interaction between genetic pred...
AIM: To identify potential biomarkers and explore the mechanisms underlying diabetic nephropathy (DN) by integrating machine learning, Mendelian randomization (MR) and experimental validation.
Single-cell transcriptome-wide association studies (scTWAS) is a new method for conducting TWAS analysis at the cellular level to identify gene-trait associations with higher precision. This approach helps overcome the challenge of interpreting cell-...