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Tuning Hyperparameters for Gene Interaction Models in Genome-Wide Association Studies

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Neural Information Processing (ICONIP 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10638))

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Abstract

In genetic epidemiology, epistasis has been the subject of several researchers to understand the underlying causes of complex diseases. Identifying gene-gene and/or gene-environmental interactions are becoming more challenging due to multiple genetic and environmental factors acting together or independently. The limitations of current computational approaches motivated the development of a deep learning method in our recent study. The approach trained a multilayered feedforward neural network to discover interacting genes associated with complex diseases. The models are evaluated under various simulated scenarios and compared with the previous methods. The results showed significant improvements in predicting gene interactions over the traditional machine learning techniques. This study is further extended to maximize the predictive performance of the method by tuning the hyperparameters using Cartesian grid and random grid searching. Several experiments are conducted on real datasets to identify higher-order interacting genes responsible for diseases. The findings demonstrated randomly chosen trials are more efficient than trials chosen by grid search for optimizing hyperparameters. The optimal configuration of hyperparameter values improved the model performance without overfitting. The results illustrate top 30 gene interactions responsible for sporadic breast cancer and hypertension.

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Authors and Affiliations

  1. Department of Computing, Curtin University, Perth, Australia

    Suneetha Uppu & Aneesh Krishna

Authors
  1. Suneetha Uppu
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  2. Aneesh Krishna
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Corresponding author

Correspondence to Suneetha Uppu .

Editor information

Editors and Affiliations

  1. Guangdong University of Technology, Guangzhou, China

    Derong Liu

  2. Guangdong University of Technology, Guangzhou, China

    Shengli Xie

  3. South China University of Technology, Guangzhou, China

    Yuanqing Li

  4. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Dongbin Zhao

  5. King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

    El-Sayed M. El-Alfy

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Uppu, S., Krishna, A. (2017). Tuning Hyperparameters for Gene Interaction Models in Genome-Wide Association Studies. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10638. Springer, Cham. https://doi.org/10.1007/978-3-319-70139-4_80

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  • DOI: https://doi.org/10.1007/978-3-319-70139-4_80

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70138-7

  • Online ISBN: 978-3-319-70139-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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