Abstract 33: Drug sensitivity prediction modeling from genomics, transcriptomics and inferred protein activity (original) (raw)

Clinical Cancer Research, 2020

Abstract

Background: Machine learning models that rely on single omics data for drug sensitivity prediction are challenging and frequently fail within precision medicine scenarios. Proteomics, for example, reflects the system biology and the regulatory network better than genomics. However, it is less likely available for many preclinical and in vivo data, mainly due to the cost of data generation. Currently, there are massive amounts of genomics data available, such as CNV, mutation, and RNA-seq signatures. However, these data do not characterize the post-translational modifications in proteins, limiting their utility for biomarker discovery. Objective: The main objective of this study is to overcome the lack of proteomics data gap. Herein, we propose a novel modeling approach to improve the prediction accuracy of drug sensitivity, that is, combining genomics and proteomics signatures. In addition to genomics and transcriptomics data, the model infers proteomic activity from gene expression...

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