Ei-Wen Yang

AI-HOPE-MAPK identifies age- and ancestry-specific MAPK pathway alterations in colorectal cancer, revealing clinically relevant mutation patterns and survival differences.

AI-HOPE-WNT is a conversational AI tool that integrates clinical and genomic data to analyze WNT pathway dysregulation in colorectal cancer, reproducing prior findings and uncovering novel associations.

AI-HOPE-RTK-RAS enables dialogue-driven analysis of RTK-RAS alterations in CRC, revealing novel associations and ancestry-specific patterns in mutation prevalence and prognosis.

The paper introduces AI-HOPE-JAK-STAT, a conversational AI platform that integrates clinical and genomic data to analyze JAK-STAT pathway alterations in colorectal cancer, revealing survival and mutational associations across patient subgroups.

AI-HOPE-PI3K enables real-time, population-aware analysis of PI3K pathway alterations in colorectal cancer using natural language queries, revealing survival differences and ancestry-linked biomarkers.

AI-HOPE-TGFbeta is a conversational AI agent that integrates clinical and genomic data to analyze TGF-β pathway alterations in colorectal cancer, revealing population-specific patterns and clinical outcomes.

AI-HOPE is a conversational AI platform that integrates clinical and genomic data using natural language, enabling analysis without programming expertise and producing statistical insights.

AI-HOPE-TP53 analyzes TP53-driven molecular alterations in colorectal cancer using natural language queries and clinical-genomic data to support precision oncology.

AI-HOPE-PM integrates genomic, clinical, and social determinants of health data using conversational AI to enable real-time, multi-dimensional cancer analysis and uncover disparities in outcomes.

The paper identifies allele-specific alternative splicing events across human tissues and develops a method to predict functional splicing-altering variants, finding many are linked to GWAS SNPs.

The paper introduces BEAPR, a computational method to detect allele-specific binding of RNA-binding proteins, showing it outperforms existing methods and identifies disease-relevant variants.