Disentangling Prognostic and Predictive Biomarkers Through Mutual Information

Konstantinos Sechidis*, Emily Turner, Paul Metcalfe, James Weatherall, Gavin Brown

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

We study information theoretic methods for ranking biomarkers. In clinical trials, there are two, closely related, types of biomarkers: predictive and prognostic, and disentangling them is a key challenge. Our first step is to phrase biomarker ranking in terms of optimizing an information theoretic quantity. This formalization of the problem will enable us to derive rankings of predictive/prognostic biomarkers, by estimating different, high dimensional, conditional mutual information terms. To estimate these terms, we suggest efficient low dimensional approximations. Finally, we introduce a new visualisation tool that captures the prognostic and the predictive strength of a set of biomarkers. We believe this representation will prove to be a powerful tool in biomarker discovery.

Original languageEnglish
Pages (from-to)141-145
Number of pages5
JournalStudies in Health Technology and Informatics
Volume235
DOIs
Publication statusPublished - 2017

Keywords

  • mutual information
  • Predictive biomarkers
  • prognostic biomarkers

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