Integrating multi-scale gene features for cancer diagnosis

Peng Hang, Mengjun Shi, Quan Long, Hui Li, Haifeng Zhao, Meng Ma

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Cancer is one of the major diseases that threaten human life. The advancement of high-throughput sequencing technology provides a way to accurately diagnose cancer and reveal the pathogenesis of cancer at the molecular level. In this study, we integrated the differentially expressed genes, and differential DNA methylation patterns, and applied multiple machine learning methods to conduct cancer diagnosis. The experimental results show that the performance of cancer diagnosis can be significantly improved with the integrated multi-scale gene features of RNA and epigenetic level. The AUC of classifier can be increased by 7.4% with multi-scale gene features compared to only differentially expressed genes, which verifies the effectiveness of the integration of multi-scale gene features for cancer diagnosis.

Original languageEnglish
Title of host publicationBiometric Recognition - 13th Chinese Conference, CCBR 2018, Proceedings
EditorsZhenan Sun, Shiguang Shan, Zhenhong Jia, Kurban Ubul, Jie Zhou, Jianjiang Feng, Zhenhua Guo, Yunhong Wang
PublisherSpringer Verlag
Number of pages10
ISBN (Print)9783319979083
StatePublished - 2018
Externally publishedYes
Event13th Chinese Conference on Biometric Recognition, CCBR 2018 - Urumchi, China
Duration: 11 Aug 201812 Aug 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10996 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference13th Chinese Conference on Biometric Recognition, CCBR 2018


  • Cancer diagnosis
  • DNA methylation
  • Gene expression
  • High-Throughput sequencing technology
  • Machine learning


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