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Abstract
Predicting cancer cell line response to drug treatment using genomic data alone is challenging due to cancer's complexity. This study integrates heterogeneous data (biological networks, genomics, inhibitor profiling, gene-disease associations) into a unified graph structure using a novel graph reduction algorithm. This algorithm improves feature entropy while preserving valuable information. Graph-based prediction of drug efficacy showed higher accuracy (0.68) compared to matrix-based methods, highlighting the benefit of non-Euclidean data representation for machine learning in cancer pharmacotherapy.
Publisher
npj Systems Biology and Applications
Published On
Apr 29, 2022
Authors
Limeng Pu, Manali Singha, Hsiao-Chun Wu, Costas Busch, J. Ramanujam, Michal Brylinski
Tags
cancer
drug response
genomic data
machine learning
graph reduction
drug efficacy
biological networks

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