Unbiased data analytic strategies to improve biomarker discovery in precision medicine

Drug Discov Today. 2019 Sep;24(9):1735-1748. doi: 10.1016/j.drudis.2019.05.018. Epub 2019 May 31.

Abstract

Omics technologies promised improved biomarker discovery for precision medicine. The foremost problem of discovered biomarkers is irreproducibility between patient cohorts. From a data analytics perspective, the main reason for these failures is bias in statistical approaches and overfitting resulting from batch effects and confounding factors. The keys to reproducible biomarker discovery are: proper study design, unbiased data preprocessing and quality control analyses, and a knowledgeable application of statistics and machine learning algorithms. In this review, we discuss study design and analysis considerations and suggest standards from an expert point-of-view to promote unbiased decision-making in biomarker discovery in precision medicine.

Publication types

  • Research Support, Non-U.S. Gov't
  • Review

MeSH terms

  • Biomarkers
  • Computational Biology / methods
  • Data Science / trends*
  • Electronic Data Processing / methods
  • Humans
  • Precision Medicine / trends*
  • Research Design / standards
  • Research Design / trends*

Substances

  • Biomarkers