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An integrated network representation of multiple cancer-specific data for graph-based machine learning

Medicine and Health

An integrated network representation of multiple cancer-specific data for graph-based machine learning

L. Pu, M. Singha, et al.

This innovative research conducted by Limeng Pu, Manali Singha, Hsiao-Chun Wu, Costas Busch, J. Ramanujam, and Michal Brylinski unveils a breakthrough in predicting cancer cell line responses to drug treatments using genomic data. By leveraging a unique graph reduction algorithm, the study enhances prediction accuracy through advanced feature representation, showcasing the power of non-Euclidean data in cancer pharmacotherapy.

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Playback language: English
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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