NDLI: Inferring domain-domain interactions using an extended parsimony model

Content Provider	IEEE Xplore Digital Library
Author	Chen Chen Jun-Fei Zhao Qiang Huang Rui-Sheng Wang Xiang-Sun Zhang
Copyright Year	2011
Description	Author affiliation: National Center for Mathematics and Interdisciplinary Sciences, Institute of Applied Mathematics, Academy of Mathematics and Systems Science, CAS, Beijing 100190, China (Chen Chen; Jun-Fei Zhao; Qiang Huang; Xiang-Sun Zhang) \|\| Department of Physics, Pennsylvania State University, University Park, 16802, USA (Rui-Sheng Wang)
Abstract	High-throughput technologies have produced a large number of protein-protein interactions (PPIs) for different species. As protein domains are functional and structural units of proteins, many computational efforts have been made to identify domain-domain interactions (DDIs) from PPIs. Parsimony assumption is widely used in computational biology as the evolution of the nature is considered as a continuous optimization process. In the context of identifying DDIs, parsimony methods try to find a minimal set of DDIs that can explain the observed PPIs. This category of methods are promising since they can be formulated and solved easily. Besides, researches have shown that they could detect specific DDIs, which is often hard for many probabilistic methods. In this paper, we revisit the parsimony model by presenting two important extensions. First, ‘complex networks’ as an emerging concept is incorporated as prior knowledge into the parsimony model. With this improvement, the prediction accuracy increases, which to some extent enhances the biological meaning of the common property of complex networks. Second, two randomization tests are designed to show the parsimony nature of the DDIs in mediating PPIs, which corroborates the model validation.
Starting Page	374
Ending Page	378
File Size	590118
Page Count	5
File Format	PDF
ISBN	9781457716614
e-ISBN	9781457716669
e-ISBN	9781457716652
DOI	10.1109/ISB.2011.6033181
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-09-02
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Proteins Protein engineering Databases Biological system modeling Computational modeling Parsimony Assumption Protein-Protein Interaction Domain-Domain Interaction Complex networks Clustering Coefficient Complex Networks Linear programming
Content Type	Text
Resource Type	Article

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