Inference Method for Reconstructing Regulatory Networks Using Statistical Path-Consistency Algorithm and Mutual Information

Yan Yan, Xinan Zhang, Tianhai Tian

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

Abstract

The advances of high-throughout technologies have produced huge amount of data regarding gene expressions or protein activities under various experimental conditions. The reverse-engineering of regulatory networks using these datasets is one of the top important research topics in computational biology. Although substantial efforts have been contributed to design effective inference methods, there are still a number of significant challenges to deal with the weak correlations between the observation data and the dependence of network structure on the order of variables in the systems. To address these issues, this work proposes a novel statistical approach to infer the structure of regulatory networks. Instead of using one single variable order, we generate a number of variable orders and then obtain different networks based on these orders. The weight of each edge for connecting genes/proteins is determined by the statistical measures based on the generated networks using different variable orders. Our proposed algorithm is evaluated by using the golden standard networks in Dream challenges and a cell signalling transduction pathway by using experimental data. Inference results suggest that our proposed algorithm is an effective approach for the reverse-engineering of regulatory networks with better accuracy.

Original languageEnglish
Title of host publicationIntelligent Computing Theories and Application
Subtitle of host publication16th International Conference, ICIC 2020, Proceedings, Part II
EditorsDe-Shuang Huang, Kang-Hyun Jo
Place of PublicationCham Switzerland
PublisherSpringer
Pages45-56
Number of pages12
ISBN (Electronic)9783030608026
ISBN (Print)9783030608019
DOIs
Publication statusPublished - 2020
EventInternational Conference on Intelligent Computing 2020 - Bari , Italy
Duration: 2 Oct 20205 Oct 2020
Conference number: 16th
https://link.springer.com/book/10.1007/978-3-030-60796-8 (Proceedings)
http://ic-ic.tongji.edu.cn/2020/index.htm (Website)

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume12464
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Conference on Intelligent Computing 2020
Abbreviated titleICIC 2020
CountryItaly
CityBari
Period2/10/205/10/20
Internet address

Keywords

  • Graphic model
  • Mutual information
  • Path-consistency
  • Regulatory network

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