Gehad Youssef

The paper shows that non-exonic read fractions reorganize lncRNA co-expression networks in renal and other cancers, with varying impacts across cancer types.

The paper identifies malignant and stromal partial EMT programs in head and neck cancer and shows that malignant measures correlate with better cetuximab outcomes.

The paper fine-tunes two scFMs on cancer subtypes and shows they outperform LightGBM on out-of-domain subtypes, with context-sensitive adaptation supported by interpretability analysis.

This paper develops a machine learning framework for therapeutic target discovery in ccRCC, identifying 16 targets and novel mechanisms with superior efficacy to standard-of-care drugs.

The paper combines stem cell models and computational predictions to identify flavin cofactors as therapeutic targets for metabolic liver dysfunction.

The paper presents an automated pipeline using NLP and chemical/disease embeddings to classify DILI-related literature, achieving high F1-scores in distinguishing DILI-positive from negative papers.

This paper introduces DILIc, an AI classifier that identifies Drug-Induced Liver Injury literature using NLP and ML, achieving high accuracy in cross-validation and external validation.