Genome-wide prediction and prioritization of human aging genes by data fusion: a machine learning approach.

Journal: BMC genomics
Published Date: Nov 9, 2019

Abstract

BACKGROUND: Machine learning can effectively nominate novel genes for various research purposes in the laboratory. On a genome-wide scale, we implemented multiple databases and algorithms to predict and prioritize the human aging genes (PPHAGE).

Authors

  • Masoud Arabfard
    Department of Bioinformatics, Kish International Campus University of Tehran, Kish, Iran.
  • Mina Ohadi
    Iranian Research Center on Aging, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran. mi.ohadi@uswr.ac.ir.
  • Vahid Rezaei Tabar
    Department of Statistics, Faculty of Mathematical Sciences and Computer, Allameh Tabataba'i University, Tehran, Iran.
  • Ahmad Delbari
    Iranian Research Center on Aging, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran.
  • Kaveh Kavousi
    Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran.

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