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Mining Multiple Biological Data for Reconstructing Signal Transduction Networks

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Data Mining: Foundations and Intelligent Paradigms

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 25))

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Abstract

Signaling transduction networks (STNs) are the key means by which a cell converts an external signal (e.g. stimulus) into an appropriate cellular response (e.g. cellular rhythms of animals and plants). The essence of STN is underlain in some signaling features scattered in various data sources and biological components overlapping among STN. The integration of those signaling features presents a challenge. Most of previous works based on PPIs for STN did not take the signaling properties of signaling molecules and components overlapping among STN into account. This paper describes an effective computational method that can exploit three biological facts of STN applied to human: protein-protein interaction networks, signaling features and sharing components. To this end, we introduce a soft-clustering method for doing the task by exploiting integrated multiple data, especially signaling features, i.e., protein-protein interactions, signaling domains, domain-domain interactions, and protein functions. The gained results demonstrated that the method was promising to discover new STN and solve other related problems in computational and systems biology from large-scale protein interaction networks. Other interesting results of the early work on yeast STN are additionally presented to show the advantages of using signaling domain-domain interactions.

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Authors and Affiliations

  1. The Microsoft Research, University of Trento Centre for Computational and Systems Biology, Piazza Manci 17, 38123, Povo, Trento, Italy

    Thanh-Phuong Nguyen

  2. School of Knowledge Science, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi, Ishikawa, 923-1292, Japan

    Tu-Bao Ho

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  1. Thanh-Phuong Nguyen
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Correspondence to Thanh-Phuong Nguyen .

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Editors and Affiliations

  1. College of Letters & Science, Dept. Statistics & Applied Probability, University of California, Santa Barbara, South Hall 5504, Santa Barbara, 93106-3110, California, USA

    Dawn E. Holmes

  2. , School of Electrical and Information Eng, University of South Australia, Adelaide,, Mawson Lakes Campus, SA 5095, Australia

    Lakhmi C Jain

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Nguyen, TP., Ho, TB. (2012). Mining Multiple Biological Data for Reconstructing Signal Transduction Networks. In: Holmes, D., Jain, L. (eds) Data Mining: Foundations and Intelligent Paradigms. Intelligent Systems Reference Library, vol 25. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23151-3_8

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  • DOI: https://doi.org/10.1007/978-3-642-23151-3_8

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