Machine learning with the TCGA-HNSC dataset: improving usability by addressing inconsistency, sparsity, and high-dimensionality.

Journal: BMC bioinformatics
Published Date: Jun 17, 2019

Abstract

BACKGROUND: In the era of precision oncology and publicly available datasets, the amount of information available for each patient case has dramatically increased. From clinical variables and PET-CT radiomics measures to DNA-variant and RNA expression profiles, such a wide variety of data presents a multitude of challenges. Large clinical datasets are subject to sparsely and/or inconsistently populated fields. Corresponding sequencing profiles can suffer from the problem of high-dimensionality, where making useful inferences can be difficult without correspondingly large numbers of instances. In this paper we report a novel deployment of machine learning techniques to handle data sparsity and high dimensionality, while evaluating potential biomarkers in the form of unsupervised transformations of RNA data. We apply preprocessing, MICE imputation, and sparse principal component analysis (SPCA) to improve the usability of more than 500 patient cases from the TCGA-HNSC dataset for enhancing future oncological decision support for Head and Neck Squamous Cell Carcinoma (HNSCC).

Authors

  • Michael C Rendleman
    Department of Electrical and Computer Engineering, Center for Bioinformatics and Computational Biology, University of Iowa, 5017 Seamans Center, Iowa City, IA, 52242, USA. michael-rendleman@uiowa.edu.
  • John M Buatti
    Department of Radiation Oncology, Carver College of Medicine, University of Iowa Carver College of Medicine, LL-W Pomerantz Family Pavilion, 200 Hawkins Drive, Iowa City, IA, 52242-1089, USA.
  • Terry A Braun
    Department of Biomedical Engineering, Center for Bioinformatics and Computational Biology, University of Iowa, 5017 Seamans Center, Iowa City, IA, 52242, USA.
  • Brian J Smith
    Department of Biostatistics, University of Iowa, 145 N. Riverside Drive, 100 CPHB, Iowa City, IA, 52242, USA.
  • Chibuzo Nwakama
    Department of Electrical and Computer Engineering, Center for Bioinformatics and Computational Biology, University of Iowa, 5017 Seamans Center, Iowa City, IA, 52242, USA.
  • Reinhard R Beichel
    Iowa Institute for Biomedical Imaging, Department of Electrical and Computer Engineering, Department of Internal Medicine, The University of Iowa, Iowa City, 52242, IA, USA.
  • Bart Brown
    Center for Bioinformatics and Computational Biology, University of Iowa, 5017 Seamans Center, Iowa City, IA, 52242, USA.
  • Thomas L Casavant
    Department of Electrical and Computer Engineering, Center for Bioinformatics and Computational Biology, University of Iowa, 5017 Seamans Center, Iowa City, IA, 52242, USA.

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