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Inference of Autism-Related Genes by Integrating Protein-Protein Interactions and miRNA-Target Interactions

  • Conference paper
Knowledge and Systems Engineering

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 244))

Abstract

Autism spectrum disorders (ASD) are a group of conditions characterized by impairments in social interaction and presence of repetitive behavior. These complex neurological diseases are among the fastest growing developmental disorders and cause varying degrees of lifelong disabilities. There have been a lot of ongoing research to unravel the pathogenic mechanism of autism. Computational methods have come to the scene as a promising approach to aid the physicians in studying autism. In this paper, we present an efficient method to predict autism-related candidate genes (autism genes in short) by integrating protein interaction network and miRNA-target interaction network. We combine the two networks by a new technique relying on shortest path calculation. To demonstrate the high performance of our method, we run several experiments on three different PPI networks extracted from the BioGRID database, the HINT database, and the HPRD database. Three supervised learning algorithms were employed, i.e., the Bayesian network and the random tree and the random forest. Among them, the random forest method performs better in terms of precision, recall, and F-measure. It shows that the random forest algorithm is potential to infer autism genes. Carrying out the experiments with five different lengths of the shortest paths in the PPI networks, the results show the advantage of the method in studying autism genes based on the large scale network. In conclusion, the proposed method is beneficial in deciphering the pathogenic mechanism of autism.

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Author information

Authors and Affiliations

  1. Hanoi National University of Education, Hanoi, Vietnam

    Dang Hung Tran

  2. The Microsoft Research, University of Trento Centre for Computational Systems Biology, Trento, Italy

    Thanh-Phuong Nguyen, Laura Caberlotto & Corrado Priami

  3. Department of Mathematics, University of Trento, Trento, Italy

    Corrado Priami

Authors
  1. Dang Hung Tran
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  2. Thanh-Phuong Nguyen
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  3. Laura Caberlotto
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  4. Corrado Priami
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Corresponding author

Correspondence to Dang Hung Tran .

Editor information

Editors and Affiliations

  1. School of Knowledge Science, Japan Advanced Institute of Science and Technology, Ishikawa, Japan

    Van Nam Huynh

  2. UMR CNRS 7253 Heudiasyc, Universite de Technologie de Compiegne, Compiegne Cedex, France

    Thierry Denoeux

  3. Faculty of Information Technology, Hanoi National University of Education, Hanoi, Vietnam

    Dang Hung Tran

  4. Faculty of Information Technology, University of Engineering and Technology, Hanoi, Vietnam

    Anh Cuong Le

  5. Faculty of Information Technology, University of Engineering and Technology, Hanoi, Vietnam

    Son Bao Pham

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© 2014 Springer International Publishing Switzerland

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Tran, D.H., Nguyen, TP., Caberlotto, L., Priami, C. (2014). Inference of Autism-Related Genes by Integrating Protein-Protein Interactions and miRNA-Target Interactions. In: Huynh, V., Denoeux, T., Tran, D., Le, A., Pham, S. (eds) Knowledge and Systems Engineering. Advances in Intelligent Systems and Computing, vol 244. Springer, Cham. https://doi.org/10.1007/978-3-319-02741-8_26

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  • DOI: https://doi.org/10.1007/978-3-319-02741-8_26

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-02740-1

  • Online ISBN: 978-3-319-02741-8

  • eBook Packages: EngineeringEngineering (R0)

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