Algorithms in Bioinformatics: 15th International Workshop, by Mihai Pop, Hélène Touzet

By Mihai Pop, Hélène Touzet

This booklet constitutes the refereed complaints of the fifteenth overseas Workshop on Algorithms in Bioinformatics, WABI 2015, held in Atlanta, GA, united states, in September 2015. The 23 complete papers offered have been conscientiously reviewed and chosen from fifty six submissions. the chosen papers conceal quite a lot of subject matters from networks to phylogenetic stories, series and genome research, comparative genomics, and RNA structure.

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Extra info for Algorithms in Bioinformatics: 15th International Workshop, WABI 2015, Atlanta, GA, USA, September 10-12, 2015, Proceedings

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Thus, we discard EC and ICS measures from further consideration, and instead, we report results for S3 . The size of the largest connected common subgraph (LCCS) [1]. In addition to counting aligned edges via S3 measure, it is important that the aligned edges cluster together to form large, dense, and connected subgraphs, rather than being isolated. In this context, a connected common subgraph (CCS) is defined as a connected subgraph (not necessarily induced) that appears in both networks [2]. We measure the size of the largest CCS (LCCS) in terms of the number of nodes as well as edges, as defined in the MAGNA paper [6].

BicNET enables the analysis of dense networks with up to 50000 nodes. Results on synthetic and real networks confirm its efficiency and relevance to discover non-trivial (yet coherent and significant) modules. Six possible directions are identified for future work: to consider further coherencies such as order-preserving and scale factors; enhance searches with scalability principles from pattern mining (data partitioning strategies and search for approximate patterns [14]); extend the proposed contributions for the integrative mining of network and expression data; explore the relevance of 14 R.

Comparison of the five NCF-AS methods on best alignments of “synthetic” (noisy yeast) networks with respect to: (a) NC, (b) S3 , (c) LCCS, and (d) Exp-GO. 80 70 60 50 40 100 90 80 70 60 50 40 30 % 25 % 20 % 15 % 10 5% % 25 % 20 % 15 % 10 5% % 25 % 20 % 15 % 10 5% % 25 % 20 % 15 % 10 5% Noise level Noise level Noise level Noise level (a) (b) (c) (d) 70 35 65 30 Exp-GO(%) 30 28 26 24 22 20 18 16 14 12 LCCS(%) S3(%) Fig. 5. Comparison of the five NCF-AS methods on best alignments of real-world PPI networks of different species with respect to: (a) S3 , (b) LCCS, and (c) Exp-GO.

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