On predicting epithelial mesenchymal transition by integrating RNA-binding proteins and correlation data via L 1/2 -regularization method

Yushan Qiu, Hao Jiang*, Wai Ki Ching, Kwok Po NG

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

Identifying tumor metastasis signatures from gene expression data at the whole genome level remains an arduous challenge, particularly so when the number of genes is huge and the number of experimental samples is small. We focus on the prediction of the epithelial-mesenchymal transition (EMT), which is an underlying mechanism of tumor metastasis, here, rather than on tumor metastasis itself, to avoid confounding effects of uncertainties derived from various factors. We apply an extended LASSO model, L 1/2 -regularization model, as a feature selector, to identify significant RNA-binding proteins (RBPs) that contribute to regulating the EMT. We find that the L 1/2 -regularization model significantly outperforms LASSO in the EMT regulation problem. Furthermore, remarkable improvement in L 1/2 -regularization model classification performance can be achieved by incorporating extra information, specifically correlation values. We demonstrate that the L 1/2 -regularization model is applicable for identifying significant RBPs in biological research. Identified RBPs will facilitate study of the underlying mechanisms of the EMT.

Original languageEnglish
Pages (from-to)96-103
Number of pages8
JournalArtificial Intelligence in Medicine
Volume95
DOIs
Publication statusPublished - Apr 2019

Scopus Subject Areas

  • Medicine (miscellaneous)
  • Artificial Intelligence

User-Defined Keywords

  • Classification
  • Epithelial-mesenchymal transition (EMT)
  • L -regularization
  • RNA-binding proteins (RBPs)

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