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dc.contributor.authorZhao, Yingwen
dc.contributor.authorWang, Jun
dc.contributor.authorGuo, Maozu
dc.contributor.authorZhang, Xiangliang
dc.contributor.authorYu, Guoxian
dc.date.accessioned2019-10-07T07:24:40Z
dc.date.available2019-10-07T07:24:40Z
dc.date.issued2019-09-24
dc.identifier.citationZhao, Y., Wang, J., Guo, M., Zhang, X., & Yu, G. (2019). Cross-Species Protein Function Prediction with Asynchronous-Random Walk. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 1–1. doi:10.1109/tcbb.2019.2943342
dc.identifier.doi10.1109/tcbb.2019.2943342
dc.identifier.urihttp://hdl.handle.net/10754/656922
dc.description.abstractProtein function prediction is a fundamental task in the post-genomic era. Available functional annotations of proteins are incomplete and the annotations of two homologous species are complementary to each other. However, how to effectively leverage mutually complementary annotations of different species to further boost the prediction performance is still not well studied. In this paper, we propose a cross-species protein function prediction approach by performing Asynchronous Random Walk on a heterogeneous network (AsyRW). AsyRW firstly constructs a heterogeneous network to integrate multiple functional association networks derived from different biological data, established homology-relationships between proteins from different species, known annotations of proteins and Gene Ontology (GO). To account for the intrinsic structures of intra- and inter-species of proteins and that of GO, AsyRW quantifies the individual walk lengths of each network node using the gravity-like theory and performs asynchronous-random walk with the individual length to predict associations between proteins and GO terms. Experiments on annotations archived in different years show that individual walk length and asynchronous-random walk can effectively leverage the complementary annotations of different species, AsyRW has a significantly improved performance to other related and competitive methods. The codes of AsyRW are available at: http://mlda.swu.edu.cn/codes.php?name=AsyRW.
dc.description.sponsorshipThis work is supported by Natural Science Foundation of China (61741217, 61872300, 61873214, 61871020, 61571163 and 61532014), Fundamental Research Funds for the Central Universities (XDJK2019B024), the National Key Research and Development Plan Task of China (Grant No. 2016YFC0901902), Natural Science Foundation of CQ CSTC (cstc2018jcyjAX0228).
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttps://ieeexplore.ieee.org/document/8847417/
dc.rights(c) 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
dc.subjectProtein function prediction
dc.subjectData fusion
dc.subjectHeterogeneous network
dc.subjectAsynchronous random walk
dc.subjectGene Ontology
dc.titleCross-Species Protein Function Prediction with Asynchronous-Random Walk
dc.typeArticle
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalIEEE/ACM Transactions on Computational Biology and Bioinformatics
dc.eprint.versionPost-print
dc.contributor.institutionCollege of Computer and Information Sciences, Southwest University, Beibei, Chongqing China
dc.contributor.institutionSchool of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, 117781 Beijing, Beijing China
dc.contributor.institutionCollege of Computer and Information Science, Southwest University, Beibei, Chongqing China 400715
kaust.personZhang, Xiangliang
refterms.dateFOA2019-10-07T07:25:16Z
dc.date.published-online2019-09-24
dc.date.published-print2019


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