Are we there yet? A machine learning architecture to predict organotropic metastases.

Journal: BMC medical genomics
Published Date: Nov 24, 2021

Abstract

BACKGROUND & AIMS: Cancer metastasis into distant organs is an evolutionarily selective process. A better understanding of the driving forces endowing proliferative plasticity of tumor seeds in distant soils is required to develop and adapt better treatment systems for this lethal stage of the disease. To this end, we aimed to utilize transcript expression profiling features to predict the site-specific metastases of primary tumors and second, to identify the determinants of tissue specific progression.

Authors

  • Michael Skaro
    Institute of Bioinformatics, University of Georgia, Athens, GA, 30602, USA. Michael.Skaro@uga.edu.
  • Marcus Hill
    Department of Computer Science, University of Georgia, Athens, GA, 30602, USA.
  • Yi Zhou
    Eye Center of Xiangya Hospital, Central South University, Changsha, Hunan, China.
  • Shannon Quinn
    Department of Computer Science, University of Georgia, Athens, GA 30602.
  • Melissa B Davis
    Caryl and Israel Englander Institute for Precision Medicine, New York Presbyterian Hospital-Weill Cornell Medicine, New York, NY, 10065, USA.
  • Andrea Sboner
    Institute for Precision Medicine.
  • Mandi Murph
    Department of Pharmaceutical and Biomedical Sciences, University of Georgia, Athens, GA, 30602, USA.
  • Jonathan Arnold
    Division of General Internal Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA arnoldjd@pitt.edu.

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