Kexin Chen

This study identifies causal genes and transcriptomic factors influencing human telomere length using placental tissue data and GWAS.

The paper develops DeepAnchor, a deep learning model to characterize CTCF binding patterns and predict CTCF-anchored loops across human tissues, revealing sequence and chromatin features that influence loop formation and disease mechanisms.

The paper identifies causal regulatory elements in complex genetic loci using CRISPR perturbations and shows that many causal variants lack classical epigenetic markers.

This paper introduces a scalable batch-correction method for integrating large-scale single-cell transcriptomes and shows it matches state-of-the-art performance on 18 million cells from the Human Cell Atlas.

The paper introduces tGPT, a generative model for integrating large-scale single-cell transcriptomes, showing its effectiveness in clustering, trajectory inference, and linking gene patterns to clinical outcomes.

The paper introduces iSEEEK, a method for integrating large-scale single-cell transcriptomes using gene rankings, showing good clustering and transferability across datasets.

This paper uses Mendelian randomization to identify causal links between coagulation factors and COVID-19 severity, finding VWF and ADAMTS13 as significant factors.

This paper develops regBase, a database of whole genome base-wise aggregations of regulatory variant predictions, and three composite models to score functional, pathogenic, and cancer driver variants, showing superior performance in fine-mapping causal variants.