An OMIC biomarker detection algorithm TriVote and its application in methylomic biomarker detection.

Abstract:

AIM:Transcriptomic and methylomic patterns represent two major OMIC data sources impacted by both inheritable genetic information and environmental factors, and have been widely used as disease diagnosis and prognosis biomarkers. MATERIALS & METHODS:Modern transcriptomic and methylomic profiling technologies detect the status of tens of thousands or even millions of probing residues in the human genome, and introduce a major computational challenge for the existing feature selection algorithms. This study proposes a three-step feature selection algorithm, TriVote, to detect a subset of transcriptomic or methylomic residues with highly accurate binary classification performance. RESULTS & CONCLUSION:TriVote outperforms both filter and wrapper feature selection algorithms with both higher classification accuracy and smaller feature number on 17 transcriptomes and two methylomes. Biological functions of the methylome biomarkers detected by TriVote were discussed for their disease associations. An easy-to-use Python package is also released to facilitate the further applications.

journal_name

Epigenomics

journal_title

Epigenomics

authors

Xu C,Liu J,Yang W,Shu Y,Wei Z,Zheng W,Feng X,Zhou F

doi

10.2217/epi-2017-0097

subject

Has Abstract

pub_date

2018-04-01 00:00:00

pages

335-347

issue

4

eissn

1750-1911

issn

1750-192X

journal_volume

10

pub_type

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