Yuchen Yang

This study designs high-fidelity CRISPR-Cas variants using a physics-driven approach combining molecular dynamics and protein language models, achieving significant improvements in fidelity with minimal experimental validation.

This study investigates alternative splicing dynamics in greater amberjack under salinity stress, finding that AS regulates distinct biological processes from differential gene expression.

This paper compares eight ST deconvolution methods using four real datasets to evaluate their performance in inferring cell type composition from spatial transcriptomics data.

This study identifies common genetic variants influencing intrinsic brain networks using rsfMRI data from 44,190 individuals, finding associations with cognitive and clinical traits.

eSCAN improves aggregate association testing in WGS data by integrating enhancer regions, enhancing statistical power and resolution in blood cell trait analysis.

HPRep quantifies reproducibility in HiChIP and PLAC-seq data by using a stratified and weighted correlation metric, outperforming existing Hi-C-based measures.

This study compares dimensionality reduction methods for bulk transcriptomic data, finding UMAP better preserves sample neighbourhood and clustering accuracy than PCA, MDS, and t-SNE.

MUNIn identifies long-range chromatin interactions across multiple samples by accounting for sequencing depth and heterogeneity, achieving lower false positives and higher power for shared peaks.

iSMNN improves batch effect correction in scRNA-seq by iteratively refining mutual nearest neighbors, enhancing cell type mixing and differential gene identification across batches.

The paper identifies cell type-specific super-interactive promoters in blood lineage cells using Hi-C data and shows their association with ATAC-seq peaks and GWAS variants.

HiC-ACT improves chromatin interaction detection from Hi-C data by post-processing independent methods, enhancing sensitivity while controlling type-I error.

FIREcaller detects frequently interacting regions from Hi-C data and shows their association with gene regulation and GWAS variants in human tissues.

SMNN improves batch effect correction in scRNA-seq by using supervised mutual nearest neighbor detection within cell types, leading to better merging and retention of cell-type specific features.

The paper identifies UV-induced DNA repair hotspots in the human genome using XR-seq and Damage-seq, showing they are associated with chromatin states and early-replication domains.

SAME-clustering improves single-cell RNA-seq clustering by combining multiple methods to produce a more accurate and diverse ensemble solution.

MAPS processes PLAC-seq and HiChIP data to identify long-range chromatin interactions by removing biases and analyzing contact frequencies.

SAFE-clustering integrates multiple clustering methods to improve accuracy and robustness in scRNA-seq data analysis, reducing cluster deviation and improving assignment compared to individual methods.