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Detecting Methylomic Biomarkers of Pediatric Autism in the Peripheral Blood Leukocytes

  • Xin Feng
  • Xubing Hao
  • Ruihao Xin
  • Xiaoqian Gao
  • Minge Liu
  • Fei Li
  • Yubo Wang
  • Ruoyao Shi
  • Shishun ZhaoEmail author
  • Fengfeng ZhouEmail author
Original research article

Abstract

Autism was a spectrum of multiple complex diseases that required an interdisciplinary group of experts to make a diagnostic decision. Both genetic and environmental factors play essential roles in causing the onset of Autism. Therefore, this study hypothesized that methylomic biomarkers may facilitate the accurate Autism detection. A comprehensive series of biomarker detection algorithms were utilized to find the best methylomic biomarkers for the Autism detection using the methylomic data of the peripheral blood samples. The best model achieved 99.70% in accuracy with 678 methylomic biomarkers and a tenfold cross validation strategy. Some of the methylomic biomarkers were experimentally confirmed to be associated with the onset or development of Autism.

Keywords

Feature selection Methylomic biomarkers Autism 

Notes

Acknowledgements

This work was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (XDB13040400), Jilin Provincial Key Laboratory of Big Data Intelligent Computing (20180622002JC), the Education Department of Jilin Province (JJKH20180145KJ), and the start-up grant of the Jilin University. This work was also partially supported by the Bioknow MedAI Institute (BMCPP-2018-001), and the High Performance Computing Center of Jilin University, China. The constructive comments from the two anonymous reviewers were greatly appreciated.

Supplementary material

12539_2019_328_MOESM1_ESM.docx (5.2 mb)
Supplementary file1 (DOCX 5286 kb)

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Copyright information

© International Association of Scientists in the Interdisciplinary Areas 2019

Authors and Affiliations

  1. 1.BioKnow Health Informatics Lab, College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of EducationJilin UniversityChangchunChina
  2. 2.BioKnow Health Informatics Lab, College of Software, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of EducationJilin UniversityChangchunChina
  3. 3.College of Life SciencesJilin UniversityChangchunChina
  4. 4.School of MathematicsJilin UniversityChangchunChina
  5. 5.College of Electronic and Information EngineeringChangchun University of Science and TechnologyChangchunChina

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