NDLI: An adaptive hybrid algorithm for global network alignment

Content Provider	ACM Digital Library
Author	Tan, Jun Xiang, Chaojuan Ma, Jin Nie, Qing Xie, Jiang Lei, Jinzhi Wen, Tieqiao
Copyright Year	2016
Description	Author Affiliation: Shanghai University, Shanghai, China(University of california-irvine and shanghai university, Shanghai, China (Nie, Qing; Tsinghua university, Beijing, China (Lei, Jinzhi); Wen, Tieqiao); Tan, Jun; Ma, Jin; Xiang, Chaojuan; Xie, Jiang)
Abstract	It is challenging to obtain reliable and optimal mapping between networks for alignment algorithms when both nodal and topological structures are taken into consideration due to the underlying NP-hard problem. Here, we introduce an adaptive hybrid algorithm that combines the classical Hungarian algorithm and the Greedy algorithm (HGA) for the global alignment of biomolecular networks. With this hybrid algorithm, every pair of nodes with one in each network is first aligned based on node information (e.g., their sequence attributes) and then followed by an adaptive and convergent iteration procedure for aligning the topological connections in the networks. For four well-studied protein interaction networks, i.e., C.elegans, yeast, D.melanogaster, and human, applications of HGA lead to improved alignments in acceptable running time. The mapping between yeast and human PINs obtained by the new algorithm has the largest value of common gene ontology (GO) terms compared to those obtained by other existing algorithms, while it still has lower Mean normalized entropy (MNE) and good performances on several other measures. Overall, the adaptive HGA is effective and capable of providing good mappings between aligned networks in which the biological properties of both the nodes and the connections are important.
Starting Page	483
Ending Page	493
Page Count	11
File Format	PDF
ISSN	15455963
Volume Number	13
Issue Number	3
Journal	IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-05-01
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Global alignment Hybrid algorithm Protein interaction network
Content Type	Text
Resource Type	Article
Subject	Genetics Biotechnology Applied Mathematics

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