Xingyi Guo

The paper performs single-cell transcriptome-wide association analysis to identify cell-type-specific genetic susceptibility genes for colorectal cancer, finding 546 gene-cell-type associations and 347 candidate susceptibility genes.

Adding OneK1K immune cell-type expression edges to an updated PrimeKG modestly improved drug-indication link prediction, AUPRC 0.816 to 0.826 overall and 0.847 to 0.864 for 40 autoimmune diseases.

The paper uses deconvolution to identify cell-type-specific DNA methylation and gene expression associations with colorectal cancer risk, finding 178 CpG sites and 68 genes across 26 new loci.

scCotag improves single-cell multi-omics integration by using co-optimal transport and regularized barycentric mapping, outperforming existing methods in alignment and embedding accuracy.

This study integrates large-scale omics and electronic health records to identify risk proteins and drugs for cancer prevention, finding 101 novel proteins and three drugs associated with reduced colorectal cancer risk.

This study identifies 60 new susceptibility loci and 441 candidate risk genes for inflammatory bowel disease through multi-ancestry GWAS and TWAS, with implications for precision medicine.

This study introduces transTF-TWAS, a method that integrates transcription factor-linked trans-located variants into transcriptome-wide association analyses to improve disease risk gene discovery.

This paper introduces edLMM, a method that detects low-effect genetic variants using moderate sample sizes, improving missing heritability estimation.

The paper introduces cLD, a statistic for measuring rare-variant linkage disequilibrium between genomic regions, and shows it identifies novel genomic interactions and associations.

The paper identifies 58 risk genes associated with common cancers by linking genetically predicted APA levels to cancer risk using GWAS data.

The paper applies transfer learning to adapt a general-purpose model to a specific domain using limited data, demonstrating improved performance in breast cancer-related tasks.

The paper identifies tissue-specific DNA methylation biomarkers for cancer risk by integrating genome and epigenome data from the GTEx consortium and GWAS data.

This paper shows that combining feature selection and aggregation in GReX limits TWAS power and adaptability, especially with low expression heritability.