Herty Liany

DruID integrates drug efficacy predictions with genetic and pharmacological data to provide personalized cancer drug recommendations, outperforming existing methods on cell line data.

ASTER identifies clinically actionable synthetic lethal gene pairs using statistical hypothesis testing on gene expression data.

The paper identifies molecular signatures and neurodevelopmental abnormalities in FMRP-deficient neurons derived from isogenic FMR1 knockout human embryonic stem cells.

The paper introduces a new network mining algorithm to identify pathway-centric interactions in cancer data, revealing dense between-pathway interactions with therapeutic potential.

The paper develops a method to predict synthetic lethal interactions by integrating heterogeneous data sources, outperforming existing methods with minimal feature engineering.

MultiDCoX identifies differentially co-expressed gene sets influenced by multiple factors in transcriptomic data, demonstrating effectiveness through simulated data and breast cancer analysis.