A machine-learning evaluation of biomarkers designed for the future of precision medicine

Precision medicine is cognizant of the impact of genetics and environments on subtypes of heterogeneous diseases and aims to identify, diagnose, and treat each subtype appropriately. Real-valued biomarkers, such as protein levels in plasma, are key for practical subtype diagnoses and hold potential to elucidate subtypes and illuminate promising drug targets. Biomarkers that are common across all subtypes have been discovered using fold change (FC) and the area under the receiver operating characteristic curve (AUC). However, FC and AUC fail to identify biomarkers for subtypes when they comprise less than half of the disease group. We present here a machine-learning biomarker evaluation method based on clustering of the data points, referred to as Difference in Bicluster Distances (DBD). We contribute efficient, yet optimal, software coupled with rigorous validation techniques, and demonstrate our approach on a late-onset Alzheimer disease (AD) gene expression dataset. Our trials produced four significant genes and appropriate thresholds for biomarker diagnostics. While none of these genes were identified as significant by either FC or AUC for the given dataset, the genes have been independently associated with AD or neurological disorders by other groups using completely independent means. In summary, DBD provides a unique and effective method for screening real-valued data to identify biomarkers associated with subtypes of heterogeneous diseases.