title: An improved method for completely uncertain biological network alignment.
abstract: With the continuous development of biological experiment technology, more and more data related to uncertain biological networks needs to be analyzed. However, most of current alignment methods are designed for the deterministic biological network. Only a few can solve the probabilistic network alignment problem. However, these approaches only use the part of probabilistic data in the original networks allowing only one of the two networks to be probabilistic. To overcome the weakness of current approaches, an improved method called completely probabilistic biological network comparison alignment (C_PBNA) is proposed in this paper. This new method is designed for complete probabilistic biological network alignment based on probabilistic biological network alignment (PBNA) in order to take full advantage of the uncertain information of biological network. The degree of consistency (agreement) indicates that C_PBNA can find the results neglected by PBNA algorithm. Furthermore, the GO consistency (GOC) and global network alignment score (GNAS) have been selected as evaluation criteria, and all of them proved that C_PBNA can obtain more biologically significant results than those of PBNA algorithm.
authors.affiliation: School of Computer Science and Engineering, MOE Key Laboratory of Computer Network and Information Integration, Southeast University, Nanjing 210096, China.
authors.forename: Bin
authors.initials: B
authors.lastname: Shen
authors.affiliation: School of Computer Science and Engineering, MOE Key Laboratory of Computer Network and Information Integration, Southeast University, Nanjing 210096, China.
authors.forename: Muwei
authors.initials: M
authors.lastname: Zhao
authors.affiliation: Division of Math and Computer Science, University of South Carolina Upstate, Spartanburg, SC 29303, USA.
authors.forename: Wei
authors.initials: W
authors.lastname: Zhong
authors.affiliation: School of Computer Science and Engineering, MOE Key Laboratory of Computer Network and Information Integration, Southeast University, Nanjing 210096, China.
authors.forename: Jieyue
authors.initials: J
authors.lastname: He
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D063990:Gene Ontology; D053263:Gene Regulatory Networks; D013997:Time Factors; D035501:Uncertainty