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Distinguishing prognostic and predictive biomarkers: an information theoretic approach

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Fig. 2. When biomarkers have both prognostic/predictive strength (M-1) VT achieves higher TPR, otherwise (M-2) the gains in TPR are vanishing. In terms of FNRProg:, VT always has very high… Click to show full abstract

Fig. 2. When biomarkers have both prognostic/predictive strength (M-1) VT achieves higher TPR, otherwise (M-2) the gains in TPR are vanishing. In terms of FNRProg:, VT always has very high error rate on selecting solely prognostic biomarkers as predictive, and it performs worse than random selection. This is the average TPR/FNRProg: over 200 simulated datasets for three different values of the predictive strength h: 1/5 means a strongly prognostic signal, 1 means equal strength between prognostic and predictive signals, and 5 means a strongly predictive signal. The sample size is 2000, and the dimensionality p1⁄430 biomarkers. Dashed lines show the TPR/FNRProg: if we were ranking the biomarkers at random. (a) M-1: Biomarkers can be both prognostic and predictive. (b) M-2: Biomarkers are solely either prognostic or predictive

Keywords: information theoretic; biomarkers information; theoretic approach; predictive biomarkers; prognostic predictive; distinguishing prognostic

Journal Title: Bioinformatics
Year Published: 2018

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