Disentangling Prognostic and Predictive Biomarkers Through Mutual Information
Sechidis, K.; Turner, E.; Metcalfe, P.; Weatherall, J.; Brown, G.
Studies in Health Technology and Informatics 235: 141-145
2017
ISSN/ISBN: 1879-8365 PMID: 28423771 Document Number: 691869
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.