By Mihai Pop, Hélène Touzet
This booklet constitutes the refereed court cases of the fifteenth overseas Workshop on Algorithms in Bioinformatics, WABI 2015, held in Atlanta, GA, united states, in September 2015. The 23 complete papers provided have been conscientiously reviewed and chosen from fifty six submissions. the chosen papers conceal a variety of themes from networks to phylogenetic reviews, series and genome research, comparative genomics, and RNA structure.
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Extra resources for Algorithms in Bioinformatics: 15th International Workshop, WABI 2015, Atlanta, GA, USA, September 10-12, 2015, Proceedings
This further validates WAVE, because NETAL implements a newer and thus possibly more eﬃcient NCF compared to NCFs of M-W or G-W, which might give NETAL unfair advantage over WAVE. 1 Methods Data We evaluate WAVE on two popular network sets [1,2,4–7]: (1) “synthetic” networks with known node mapping, and (2) real-world networks with unknown node mapping. The “synthetic” data consists of a high-conﬁdence yeast PPI network  with 1,004 nodes and 8,323 PPIs, and of ﬁve noisy networks constructed by adding to the high-conﬁdence network a percentage of low-conﬁdence PPIs from the same data set ; we vary the percentage from 5 % to 25 % in increments of 5 %.
Thus, results do not change from topology-only to best alignments when comparing the three methods: WAVE remains superior to NETAL and MAGNA (Figs. 7 (b) and 8). Again, overall, the ranking of the methods does not change with increase in noise level. Networks With Unknown Node Mapping 100 90 3 LCCS(%) 80 70 60 50 40 30 100 95 90 85 80 75 70 65 60 55 50 33 100 90 Exp-GO(%) 100 90 80 70 60 50 40 30 20 10 S (%) NC(%) Simultaneous Optimization of both Node and Edge Conservation 80 70 60 50 40 % % 25 % 20 % 10 15 5% % 25 % 20 % 15 % 10 5% % % 25 % 20 % 15 5% 10 % % 25 % 20 % 15 5% 10 Noise level Noise level Noise level Noise level (a) (b) (c) (d) 75 35 40 70 30 35 65 30 25 Exp-GO(%) 45 LCCS(%) S3(%) Fig.
Proteins Struct. Funct. Bioinf. 54(1), 49–57 (2004) 28. : Discovering molecular pathways from protein interaction and gene expression data. Bioinformatics 19(suppl 1), i264–i272 (2003) 29. : Network-based prediction of protein function. Mol. Syst. Biol. 3(1), 88 (2007) 30. : Protein complexes and functional modules in molecular networks. Natl. Acad. Sci. 100(21), 12123–12128 (2003) 31. : String v10: proteinprotein interaction networks, integrated over the tree of life. Nucleic Acids Res. 43, D447–D452 (2014).
Algorithms in Bioinformatics: 15th International Workshop, WABI 2015, Atlanta, GA, USA, September 10-12, 2015, Proceedings by Mihai Pop, Hélène Touzet