Mengyuan Yang

CLCluster uses contrastive learning with multi-omics data to cluster cancer subtypes, showing improved performance over existing methods and identifying AS events linked to patient survival.

The paper introduces two LD estimation algorithms to create high-resolution LD grids across species, revealing evolutionary patterns and population structure.

This study performs GWAS on BMD traits and fracture risk using 30,000 UK Biobank samples, identifies novel loci, and prioritizes drug targets for osteoporosis prevention.

This paper introduces encG-reg, an encrypted genotype regression method that identifies relatives across cohorts without sharing raw data, achieving accuracy comparable to traditional methods.

The paper introduces SEAD, an Asian reference panel that improves rare variant imputation and GWAS analysis, identifying a novel association between SNTG1 and hip BMD.

This study investigates how A-to-I RNA editing affects cancer immunity by modulating interferon pathways and immune cell infiltration.

The paper identifies genetic variants influencing A-to-I RNA editing in neurodegenerative diseases using large-scale transcriptomic and genetic data from multiple brain tissues.

CCPE estimates cell cycle pseudotime and identifies cell cycle phases from scRNA-seq data, showing effectiveness and robustness in simulated and real datasets.

This study introduces FusionAI, a deep learning model that predicts gene fusion breakpoints from DNA sequences, aiding in the identification of genomic breakage mechanisms.

The paper presents genomic analyses of 10,376 Chinese individuals, identifying novel genetic variations and providing a reference haplotype map, along with an imputation server for improved accuracy.

This study introduces ESOCVD, a computational platform to detect circadian gene expression patterns in AD using untimed transcriptome data, revealing five circadian alteration patterns in AD patients.