Work

Evaluation of GPT-3 for Anti-Cancer Drug Sensitivity Prediction

Year: 2023

Type: preprint

Abstract: In this study, we investigated the potential of GPT-3 for the anti-cancer drug sensitivity prediction task using structured pharmacogenomics data across five tissue types and evaluated its performance with zero-shot prompting and fine-tuning paradigms. The drug's smile representation and cell line's genomic mutation features were predictive of the drug response. The results from this study have the potential to pave the way for designing more efficient treatment protocols in precision oncology.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2309.10016

Authors: Chowdhury, Shaika, Rajaganapathy, Sivaraman, Sun, Lichao, Cerhan, James, Zong, Nansu

Institutions: Mayo Clinic, Lehigh University

Language: English

Topic: Computational Drug Discovery Methods

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Institutes of Health, National Institute of General Medical Sciences