03230nam 2200709 450 991081939110332120210429200357.01-5015-0150-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.MiAaPQMiAaPQMiAaPQBOOK9910819391103321Machine learning for protein subcellular localization prediction4049568UNINA