Luis E. Tafoya

MutQA: A Cross-Validated Q&A Dataset for Genetic Mutations

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.

Tumor γδ T-cell abundance is associated with favorable cancer treatment outcomes

This study finds that higher pretreatment γδ T-cell abundance is linked to better outcomes in cancer patients receiving immune checkpoint blockade and improved survival across multiple tumor types.

DeepVul: A Multi-Task Transformer Model for Joint Prediction of Gene Essentiality and Drug Response

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: Leveraging Multi-Relational Graph Neural Networks and Text Knowledge for Biomedical Predictions

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: a transformer-based model that uses contrastive learning to integrate textual information into gene representations

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