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The process of biomarker discovery is typically lengthy and costly, involvingthe phases of discovery, qualification, verification, and validation beforeclinical evaluation. Being able to efficiently identify the truly relevantmarkers in discovery studies can significantly simplify the process. However,in discovery studies the sample size is typically small while the number ofmarkers being explored is much larger. Hence discovery studies suffer fromsparsity and high dimensionality issues. Currently the state-of-the-art methodseither find too many false positives or fail to identify many truly relevantmarkers. In this paper we develop a likelihood ratio-based approach and aim foraccurately finding the truly relevant protein markers in discovery studies. Ourmethod fits especially well with discovery studies because they are mostlybalanced design due to the fact that experiments are limited and controlled.Our approach is based on the observation that the underlying distributions ofexpression profiles are unimodal for those irrelevant plain markers. Our methodhas asymptotic chi-square null distribution which facilitates the efficientcontrol of false discovery rate. We then evaluate our method using bothsimulated and real experimental data. In all the experiments our method ishighly effective to discover the set of truly relevant markers, leading toaccurate biomarker identifications with high sensitivity and low empiricalfalse discovery rate.

A Likelihood Ratio Approach for Precise Discovery of Truly Relevant Protein Markers