Mining multiple biological data for reconstructing signal transduction networks

Material type: 図書

Author: Nguyen, Thanh-Phuongほか

Publisher: Springer

Publication date: 2012

Material Format: Digital

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Note (General)：: Signaling transduction networks (STNs) are the key means by which a cell converts an external signal (e.g. stimulus) into an appropriate cellular resp...

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Material Type: 図書
Title: Mining multiple biological data for reconstructing signal transduction networks
Author/Editor: Nguyen, Thanh-Phuong
Ho, Tu-Bao
Author Heading: Nguyen, Thanh-Phuong
Ho, Tu-Bao
Publication, Distribution, etc.: Springer
Publication Date: 2012
Publication Date (W3CDTF): 2012
No. or year of volume/issue: 25
Volume: 25
Pages: 163-185
ISSN (Periodical Title): ISSN : 1868-4394
Text Language Code: eng
Subject Heading: signal transduction network reconstruction
multiple biological data
Target Audience: 一般
Note (General): 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.
identifier:https://dspace.jaist.ac.jp/dspace/handle/10119/10335
Access Restrictions: 限定公開
Rights (production): This is the author-created version of Springer, Thanh-Phuong Nguyen and Tu-Bao Ho, Data Mining: Foundations and Intelligent Paradigms, 25, 2012, pp.163-185. The original publication is available at www.springerlink.com, http://dx.doi.org/10.1007/978-3-642-23151-3_8
Related Material: Data Mining: Foundations and Intelligent Paradigms
Related Material (DOI): 10.1007/978-3-642-23151-3_8
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Original Data Provider (Database): 北陸先端科学技術大学院大学 : JAIST学術研究成果リポジトリ