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International Symposium on Bioinformatics Research and Applications

ISBRA 2020: Bioinformatics Research and Applications pp 56–67Cite as

EpIntMC: Detecting Epistatic Interactions Using Multiple Clusterings

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Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 12304))

Abstract

Detecting epistatic interaction between multiple single nucleotide polymorphisms (SNPs) is crucial to identify susceptibility genes associated with complex human diseases. Stepwise search approaches have been extensively studied to greatly reduce the search space for follow-up SNP interactions detection. However, most of these stepwise methods are prone to filter out significant polymorphism combinations and thus have a low detection power. In this paper, we propose a two-stage approach called EpIntMC, which uses multiple clusterings to significantly shrink the search space and reduce the risk of filtering out significant combinations for the follow-up detection. EpIntMC firstly introduces a matrix factorization based approach to generate multiple diverse clusterings to group SNPs into different clusters from different aspects, which helps to more comprehensively explore the genotype data and reduce the chance of filtering out potential candidates overlooked by a single clustering. In the search stage, EpIntMC applies Entropy score to screen SNPs in each cluster, and uses Jaccard similarity to merge the most similar clusters into candidate sets. After that, EpIntMC uses exhaustive search on these candidate sets to precisely detect epsitatic interactions. Extensive simulation experiments show that EpIntMC has a higher (comparable) power than related competitive solutions, and results on Wellcome Trust Case Control Consortium (WTCCC) dataset also expresses its effectiveness.

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Acknowledgements

This research is supported by NSFC (61872300), Fundamental Research Funds for the Central Universities (XDJK2020B028 and XDJK2019B024), Natural Science Foundation of CQ CSTC (cstc2018jcyjAX0228).

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Authors and Affiliations

  1. College of Computer and Information Science, Southwest University, Chongqing, 400715, China

    Huiling Zhang, Guoxian Yu, Wei Ren & Jun Wang

  2. College of Electronics and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing, 100044, China

    Maozu Guo

Authors
  1. Huiling Zhang
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  2. Guoxian Yu
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  3. Wei Ren
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  4. Maozu Guo
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  5. Jun Wang
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Corresponding author

Correspondence to Jun Wang .

Editor information

Editors and Affiliations

  1. Department of Computer Science, Georgia State University, Atlanta, GA, USA

    Zhipeng Cai

  2. University of Connecticut, Storrs Mansfield, CT, USA

    Ion Mandoiu

  3. Florida International University, Miami, FL, USA

    Giri Narasimhan

  4. Georgia State University, Atlanta, GA, USA

    Pavel Skums

  5. University of North Texas, Denton, TX, USA

    Xuan Guo

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Zhang, H., Yu, G., Ren, W., Guo, M., Wang, J. (2020). EpIntMC: Detecting Epistatic Interactions Using Multiple Clusterings. In: Cai, Z., Mandoiu, I., Narasimhan, G., Skums, P., Guo, X. (eds) Bioinformatics Research and Applications. ISBRA 2020. Lecture Notes in Computer Science(), vol 12304. Springer, Cham. https://doi.org/10.1007/978-3-030-57821-3_6

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  • DOI: https://doi.org/10.1007/978-3-030-57821-3_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-57820-6

  • Online ISBN: 978-3-030-57821-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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