Automatic parameter learning for multiple network alignment
RECOMB'08 Proceedings of the 12th annual international conference on Research in computational molecular biology
Dividing protein interaction networks for modular network comparative analysis
Pattern Recognition Letters
Identification of conserved protein complexes by module alignment
International Journal of Data Mining and Bioinformatics
Scalable multiple global network alignment for biological data
Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine
A novel framework for large scale metabolic network alignments by compression
Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine
Prioritizing disease genes by bi-random walk
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Scalable test data generation from multidimensional models
Proceedings of the ACM SIGSOFT 20th International Symposium on the Foundations of Software Engineering
Probabilistic Biological Network Alignment
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
| Hi-index | 3.84 |
Motivation: With more and more data on molecular networks (e.g. protein interaction networks, gene regulatory networks and metabolic networks) available, the discovery of conserved patterns or signaling pathways by comparing various kinds of networks among different species or within a species becomes an increasingly important problem. However, most of the conventional approaches either restrict comparative analysis to special structures, such as pathways, or adopt heuristic algorithms due to computational burden. Results: In this article, to find the conserved substructures, we develop an efficient algorithm for aligning molecular networks based on both molecule similarity and architecture similarity, by using integer quadratic programming (IQP). Such an IQP can be relaxed into the corresponding quadratic programming (QP) which almost always ensures an integer solution, thereby making molecular network alignment tractable without any approximation. The proposed framework is very flexible and can be applied to many kinds of molecular networks including weighted and unweighted, directed and undirected networks with or without loops. Availability: Matlab code and data are available from http://zhangroup.aporc.org/bioinfo/MNAligner or http://intelligent.eic.osaka-sandai.ac.jp/chenen/software/MNAligner, or upon request from authors. Contact:zxs@amt.ac.cn, chen@eic.osaka-sandai.ac.jp Supplementary information: Supplementary data are available at Bioinformatics online.