Assessing reproducibility and veracity across machine learning techniques in biomedicine: A case study using TCGA data.

Journal: International journal of medical informatics
Published Date: May 13, 2020

Abstract

BACKGROUND: Many studies that aim to identify gene biomarkers using statistical methods and translate them into FDA-approved drugs have faced challenges due to lack of clinical validity and methodological reproducibility. Since genomic data analysis relies heavily on these statistical learning tools more than before, it is vital to address the limitations of these computational techniques.

Authors

  • Ahyoung Amy Kim
    Graduate Interdisciplinary Program in Statistics and Data Science, The University of Arizona, United States. Electronic address: akim127@email.arizona.edu.
  • Samir Rachid Zaim
    Graduate Interdisciplinary Program in Statistics and Data Science, The University of Arizona, United States; Center for Biomedical Informatics & Biostatistics, University of Arizona Health Sciences, United States; Department of Medicine, College of Medicine-Tucson, The University of Arizona, United States.
  • Vignesh Subbian
    Department of Electrical Engineering and Computing Systems, University of Cincinnati, Cincinnati, OH.

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