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Crawford et al. Algorithms Mol Biol (2015) 10:19 DOI 10.1186/s13015-015-0050-8
| Content Provider | CiteSeerX |
|---|---|
| Abstract | Fair evaluation of global network aligners Joseph Crawford1, Yihan Sun1,2,3 and Tijana Milenković1* Background: Analogous to genomic sequence alignment, biological network alignment identifies conserved regions between networks of different species. Then, function can be transferred from well- to poorly-annotated species between aligned network regions. Network alignment typically encompasses two algorithmic components: node cost function (NCF), which measures similarities between nodes in different networks, and alignment strategy (AS), which uses these similarities to rapidly identify high-scoring alignments. Different methods use both different NCFs and different ASs. Thus, it is unclear whether the superiority of a method comes from its NCF, its AS, or both. We already showed on state-of-the-art methods, MI-GRAAL and IsoRankN, that combining NCF of one method and AS of another method can give a new superior method. Here, we evaluate MI-GRAAL against a newer approach, GHOST, by mixing-and-matching the methods ’ NCFs and ASs to potentially further improve alignment quality. While doing so, we approach important questions that have not been asked systematically thus far. First, we ask how much of the NCF information should come from protein sequence data compared to network topology data. Existing methods |
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| Access Restriction | Open |
| Subject Keyword | High-scoring Alignment Method Ncfs Protein Sequence Data Global Network Aligners Joseph Crawford1 Tijana Milenkovi Network Topology Data Node Cost Function Different As Fair Evaluation Yihan Sun1 Network Region Alignment Strategy Alignment Quality Algorithm Mol Biol Ncf Information Different Ncfs Algorithmic Component Biological Network Alignment Identifies Poorly-annotated Specie New Superior Method |
| Content Type | Text |
