Embedded Files
Software and computational frameworks developed by BOSE-AI lab
An open-source R package for sex-aware genome-wide association studies (GWAS) that provides comprehensive workflows for quality control, sex-stratified and X chromosome analyses, heritability estimation, polygenic risk scoring, genetic correlation, and sex-differential association testing.
A multiplex heterogeneous network framework for predicting patient-specific drug responses by integrating molecular, pharmacological, and clinical information within a unified network-based machine learning framework.
Computational tools for prioritizing biologically relevant preclinical models by matching cancer cell lines to patient tumors using integrated molecular and pathway-level similarity analyses, thereby improving translational cancer research.
A computational framework for identifying regulatory microRNA–gene interactions in cancer by integrating multi-omics data to uncover biologically relevant regulatory networks and prognostic biomarkers.
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