Exploiting temporal genetic correlations for enhancing regulatory network optimization

Ahammed Sherief Kizhakkethil Youseph, Madhu Chetty, Gour Karmakar

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    Abstract

    Inferring gene regulatory networks (GRN) from microarray gene expression data is a highly challenging problem in computational and systems biology. To make GRN reconstruction process more accurate and faster, in this paper, we develop a technique to identify the gene having maximum in-degree in the network using the temporal correlation of gene expression profiles. The in-degree of the identified gene is estimated applying evolutionary optimization algorithm on a decoupled S-system GRN model. The value of in-degree thus obtained is set as the maximum in-degree for inference of the regulations in other genes. The simulations are carried out on in silico networks of small and medium sizes. The results show that both the prediction accuracy in terms of well known performance metrics and the computational time of the optimization process have been improved when compared with the traditional S-system model based inference.

    Original languageEnglish
    Title of host publicationNeural Information Processing
    Subtitle of host publication23rd International Conference, ICONIP 2016, Kyoto, Japan, October 16–21, 2016, Proceedings, Part IV
    PublisherSpringer
    Pages479-487
    Number of pages9
    Volume9947 LNCS
    ISBN (Print)9783319466866
    DOIs
    Publication statusPublished - 2016
    EventInternational Conference on Neural Information Processing 2016 - Kyoto, Japan
    Duration: 16 Oct 201621 Oct 2016
    Conference number: 23rd
    https://link.springer.com/conference/iconip

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume9947 LNCS
    ISSN (Print)03029743
    ISSN (Electronic)16113349

    Conference

    ConferenceInternational Conference on Neural Information Processing 2016
    Abbreviated titleICONIP 2016
    CountryJapan
    CityKyoto
    Period16/10/1621/10/16
    Otherfour-volume proceedings of the 23rd International Conference
    on Neural Information Processing (ICONIP 2016) held in Kyoto, Japan, during
    October 16–21, 2016

    Lecture Notes in Computer Science 9947
    Neural Information Processing
    23rd International Conference, ICONIP 2016
    Kyoto, Japan, October 16–21, 2016
    Proceedings, Part I
    ISSN 0302-9743 ISSN 1611-3349 (electronic)
    Lecture Notes in Computer Science
    ISBN 978-3-319-46686-6 ISBN 978-3-319-46687-3 (eBook)
    DOI 10.1007/978-3-319-46687-3
    Internet address

    Keywords

    • Differential evolution
    • Discrete cosine transform
    • Gene regulatory network
    • S-system
    • Temporal correlation

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