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European Conference on Genetic Programming (Part of EvoStar)

EuroGP 2020: Genetic Programming pp 279–294Cite as

Classification of Autism Genes Using Network Science and Linear Genetic Programming

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12101))

Abstract

Understanding the genetic background of complex diseases and disorders plays an essential role in the promising precision medicine. Deciphering what genes are associated with a specific disease/disorder helps better diagnose and treat it, and may even prevent it if predicted accurately and acted on effectively at early stages. The evaluation of candidate disease-associated genes, however, requires time-consuming and expensive experiments given the large number of possibilities. Due to such challenges, computational methods have seen increasing applications in predicting gene-disease associations. Given the intertwined relationships of molecules in human cells, genes and their products can be considered to form a complex molecular interaction network. Such a network can be used to find candidate genes that share similar network properties with known disease-associated genes. In this research, we investigate autism spectrum disorders and propose a linear genetic programming algorithm for autism gene prediction using a human molecular interaction network and known autism-genes for training. We select an initial set of network properties as features and our LGP algorithm is able to find the most relevant features while evolving accurate predictive models. Our research demonstrates the powerful and flexible learning abilities of GP on tackling a significant biomedical problem, and is expected to inspire further exploration of wide GP applications.

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Acknowledgments

This research was supported by the Natural Science and Engineering Research Council (NSERC) of Canada Discovery Grant RGPIN-2016-04699 to TH.

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Authors and Affiliations

  1. Department of Computer Science, Memorial University, St. John’s, NL, A1B 3X5, Canada

    Yu Zhang, Yuanzhu Chen & Ting Hu

  2. School of Computing, Queen’s University, Kingston, ON, K7L 2N8, Canada

    Ting Hu

Authors
  1. Yu Zhang
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  2. Yuanzhu Chen
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  3. Ting Hu
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Correspondence to Ting Hu .

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Editors and Affiliations

  1. Queen's University, Kingston, ON, Canada

    Ting Hu

  2. University of Coimbra, Coimbra, Portugal

    Nuno Lourenço

  3. University of Trieste, Trieste, Italy

    Eric Medvet

  4. Pablo de Olavide University, Seville, Spain

    Federico Divina

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Zhang, Y., Chen, Y., Hu, T. (2020). Classification of Autism Genes Using Network Science and Linear Genetic Programming. In: Hu, T., Lourenço, N., Medvet, E., Divina, F. (eds) Genetic Programming. EuroGP 2020. Lecture Notes in Computer Science(), vol 12101. Springer, Cham. https://doi.org/10.1007/978-3-030-44094-7_18

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  • DOI: https://doi.org/10.1007/978-3-030-44094-7_18

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