02194nas 2200517- 450 99632090260331620210927213032.0(OCoLC)608228705(CKB)2320000000014958(CONSER)--2010204404(EXLCZ)99232000000001495820100421a20109999 --- aengur|||||||||||txtrdacontentcrdamediacrrdacarrierJournal of western archives[Logan, UT] :[Utah State University],[2010-]1 online resourceRefereed/Peer-reviewed2154-7149 Focuses on contemporary issues and developments in the archival and curatorial fields, particularly as they affect Western archives and manuscript repositories. Submissions that provide insights on how technological changes have affected archival theory and practice are welcome, as are those that consider collaborative efforts and projects between different cultural heritage institutions. The history of archives/special collections and the development of the archival and curatorial professions in the western United States are also of particular interest.JWAWesternArchivesJ. western archivesArchivesWest (U.S.)PeriodicalsArchivesTechnological innovationsWest (U.S.)PeriodicalsArchival resourcesfast(OCoLC)fst00814016Archivesfast(OCoLC)fst00814030ArchivesTechnological innovationsfast(OCoLC)fst00814093West (U.S.)Archival resourcesPeriodicalsWest United StatesfastPeriodicals.fastArchives, Ancient Documents & SealsArchivesArchivesTechnological innovationsArchival resources.Archives.ArchivesTechnological innovations.027.007Utah State University.JOURNAL996320902603316Journal of western archives2576914UNISA03230nam 2200709 450 991079713960332120210429200357.01-5015-0150-X1-5015-0152-610.1515/9781501501500(CKB)3710000000420347(EBL)1820373(SSID)ssj0001482330(PQKBManifestationID)12496229(PQKBTitleCode)TC0001482330(PQKBWorkID)11508592(PQKB)10943492(DE-B1597)444958(OCoLC)912323205(DE-B1597)9781501501500(Au-PeEL)EBL1820373(CaPaEBR)ebr11059834(CaONFJC)MIL808157(OCoLC)910408036(CaSebORM)9781501501524(MiAaPQ)EBC1820373(EXLCZ)99371000000042034720150609h20152015 uy 0engur|nu---|u||utxtccrMachine learning for protein subcellular localization prediction /Shibiao Wan, Man-Wai MakBerlin, Germany ;Boston, Massachusetts :De Gruyter,2015.©20151 online resource (210 p.)Description based upon print version of record.1-5015-1048-7 Includes bibliographical references and index.Front matter --Preface --Contents --List of Abbreviations --1. Introduction --2. Overview of subcellular localization prediction --3. Legitimacy of using gene ontology information --4. Single-location protein subcellular localization --5. From single- to multi-location --6. Mining deeper on GO for protein subcellular localization --7. Ensemble random projection for large-scale predictions --8. Experimental setup --9. Results and analysis --10. Properties of the proposed predictors --11. Conclusions and future directions --A. Webservers for protein subcellular localization --B. Support vector machines --C. Proof of no bias in LOOCV --D. Derivatives for penalized logistic regression --Bibliography --IndexComprehensively covers protein subcellular localization from single-label prediction to multi-label prediction, and includes prediction strategies for virus, plant, and eukaryote species. Three machine learning tools are introduced to improve classification refinement, feature extraction, and dimensionality reduction.ProteinsPhysiological transportData processingMachine learningProbabilitiesData processingBioinformatics.Computer Science.Proteomics.ProteinsPhysiological transportData processing.Machine learning.ProbabilitiesData processing.572/.696WC 7700rvkWan Shibiao1128379Mak M. W.MiAaPQMiAaPQMiAaPQBOOK9910797139603321Machine learning for protein subcellular localization prediction3751463UNINA