Joint learning improves protein abundance prediction in cancers.

Journal: BMC biology
Published Date: Dec 23, 2019

Abstract

BACKGROUND: The classic central dogma in biology is the information flow from DNA to mRNA to protein, yet complicated regulatory mechanisms underlying protein translation often lead to weak correlations between mRNA and protein abundances. This is particularly the case in cancer samples and when evaluating the same gene across multiple samples.

Authors

  • Hongyang Li
    Department of Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, MI, 48109, USA. hyangl@umich.edu.
  • Omer Siddiqui
    Department of Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, MI, 48109, USA.
  • Hongjiu Zhang
    Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
  • Yuanfang Guan
    Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA. gyuanfan@umich.edu.

Keywords

External Resources

Popular Topics
Recent Journals