NDLI: A Bayesian framework for combining protein and network topology information for predicting protein-protein interactions

Content Provider	ACM Digital Library
Author	Birlutiu, Adriana D'alché-buc, Florence Heskes, Tom
Copyright Year	2015
Description	Author Affiliation: IBISC, Université d'Evry-Val d'Essonne, Genopole, France(Institute for computing and information sciences, Radboud University Nijmegen, The Netherlands (Heskes, Tom; Institute for computing and information sciences, Radboud University Nijmegen, The Netherlands and Faculty of Science, "1 Decembrie 1918" University, Alba-Iulia, Romania (Birlutiu, Adriana); Dalch-buc, Florence))
Abstract	Computational methods for predicting protein-protein interactions are important tools that can complement high-throughput technologies and guide biologists in designing new laboratory experiments. The proteins and the interactions between them can be described by a network which is characterized by several topological properties. Information about proteins and interactions between them, in combination with knowledge about topological properties of the network, can be used for developing computational methods that can accurately predict unknown protein-protein interactions. This paper presents a supervised learning framework based on Bayesian inference for combining two types of information: i) network topology information, and ii) information related to proteins and the interactions between them. The motivation of our model is that by combining these two types of information one can achieve a better accuracy in predicting protein-protein interactions, than by using models constructed from these two types of information independently.
Starting Page	538
Ending Page	550
Page Count	13
File Format	PDF
ISSN	15455963
DOI	10.1109/TCBB.2014.2359441
Volume Number	12
Issue Number	3
Journal	IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-05-01
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Bayesian methods Network analysis Protein-protein interaction Topology
Content Type	Text
Resource Type	Article
Subject	Genetics Biotechnology Applied Mathematics

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