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Causality Inference Techniques for In-Silico Gene Regulatory Network

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Mining Intelligence and Knowledge Exploration

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8284))

Abstract

Causality detection in gene regulatory networks (GRN) is a challenging problem due to the limit of available data and lack of efficiency in the existing techniques. A number of techniques proposed so far to reconstruct GRN. However, majority of them ignore drawing causality among genes which indicates regulatory relationship. In this paper, we study few techniques available for inferring causality. We select four state-of-the-art causality detection techniques namely, Bayesian network, Granger causality, Mutual information(MI) and Transfer entropy based approach for our study. Performance of the techniques are evaluated using DREAM challenge data based on associated in-silico regulatory networks. Experimental results reveal the superiority of MI based approach in terms of prediction accuracy in comparison to other techniques.

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Author information

Authors and Affiliations

  1. Dept of IT, North Eastern Hill University, Shillong, 793022, Meghalaya, India

    Swarup Roy, Dipankar Das, Dhrubajyoti Choudhury, Gunenja G. Gohain & Ramesh Sharma

  2. Dept of CSE, Tezpur University, Napaam, 784028, Assam, India

    Dhruba K. Bhattacharyya

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  1. Swarup Roy
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  2. Dipankar Das
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  3. Dhrubajyoti Choudhury
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  4. Gunenja G. Gohain
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  5. Ramesh Sharma
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  6. Dhruba K. Bhattacharyya
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Editors and Affiliations

  1. Department of Business Information Systems, FSIC, National University of Ireland, University College Cork, O’Rahilly Buildings, Cork, Ireland

    Rajendra Prasath

  2. Research Centre in Computer Science, V.H.N.Senthikumara Nadar College (Autonomous), 626 001, Virudhunagar, Tamil Nadu, India

    T. Kathirvalavakumar

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Roy, S., Das, D., Choudhury, D., Gohain, G.G., Sharma, R., Bhattacharyya, D.K. (2013). Causality Inference Techniques for In-Silico Gene Regulatory Network. In: Prasath, R., Kathirvalavakumar, T. (eds) Mining Intelligence and Knowledge Exploration. Lecture Notes in Computer Science(), vol 8284. Springer, Cham. https://doi.org/10.1007/978-3-319-03844-5_44

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  • DOI: https://doi.org/10.1007/978-3-319-03844-5_44

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-03843-8

  • Online ISBN: 978-3-319-03844-5

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