David Arredondo

The paper introduces MutQA, a cross-validated Q&A dataset linking genetic mutations to their functional consequences, and demonstrates its utility in improving LLM performance.

DeepVul predicts gene essentiality and drug response using a multi-task transformer model trained on cancer transcriptome data, improving precision oncology by identifying treatment vulnerabilities.

MedGraphNet integrates multi-relational biological data with text knowledge to improve biomedical predictions, outperforming single-relation methods and identifying drug side effects without direct training.

LitGene integrates textual information into gene representations using a transformer-based model with contrastive learning, improving disease risk gene prediction and interpretability.