Kaixian Yu

Alzheimer Disease Knowledge Graph Enhances Knowledge Discovery and Disease Prediction

The paper constructs an Alzheimer's Disease Knowledge Graph (ADKG) to enhance disease prediction and knowledge discovery, showing improved performance in predictive modeling.

iKraph: a comprehensive, high-quality biomedical knowledge graph for AI-powered, data-driven biomedical research

The paper constructs a comprehensive biomedical knowledge graph (iKraph) using PubMed abstracts and integrates it with public databases to enable AI-powered knowledge discovery, demonstrating its utility in drug repurposing.

Subclonal mutation load predicts survival and response to immunotherapy in cancers with low to moderate tumor mutation burden

The paper develops CliPP to estimate subclonal mutation load (sML) and shows it predicts survival and immunotherapy response in cancers with low to moderate TMB.

Scalable subclonal reconstruction of cancer cells in DNA sequencing data using a penalized likelihood model

CliPP reconstructs subclonal architecture in cancer DNA sequencing data efficiently and accurately, outperforming existing methods in speed while maintaining high concordance with consensus calls.

Tumor cell total mRNA expression shapes the molecular and clinical phenotype of cancer

The paper quantifies tumor-specific total mRNA expression from bulk data and shows its association with clinical outcomes across 15 cancer types.

GRASP: a Bayesian network structure learning method using adaptive sequential Monte Carlo

GRASP is a Bayesian network structure learning method that uses adaptive sequential Monte Carlo to improve network discovery from genomic data.