Classification Based on Feature Extraction for Hepatocellular Carcinoma Diagnosis Using High-throughput Dna Methylation Sequencing Data

Zhiyuan Yang, Meng Jin, Zhongyang Zhang, Jianwei Lu, Ke Hao

Research output: Contribution to journalConference articlepeer-review

14 Scopus citations

Abstract

DNA methylation is a well-studied mechanism of epigenetic regulation, which plays an important role in oncogenesis and tumor progression. Even at very early stage, cancer genome exhibits aberrant methylation patterns, such as hypermethylation and hypomethylation at different scales. The detection of abnormal methylation patterns with whole-genome bisulfite sequencing (WGBS) using circulating DNA from plasma has become a promising method for cancer diagnosis. In this study, Boruta, an extension of the random forest, was used to select important features (variables). Those selected features were used to establish a support vector machine (SVM) classifier for liver cancer diagnosis. As the results, a WGBS data set from hepatocellular carcinoma (HCC) patients was employed to show the improved performance of the proposed method for diagnosis.

Original languageEnglish
Pages (from-to)412-417
Number of pages6
JournalProcedia Computer Science
Volume107
DOIs
StatePublished - 2017
Event7th International Congress of Information and Communication Technology, ICICT 2017 - Sanya, China
Duration: 1 Jan 20172 Feb 2017

Keywords

  • Boruta
  • Classification
  • DNA methylation
  • SVM

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