LEADER 03130nam 2200673 450 001 9910460442103321 005 20210429200357.0 010 $a1-5015-0150-X 010 $a1-5015-0152-6 024 7 $a10.1515/9781501501500 035 $a(CKB)3710000000420347 035 $a(EBL)1820373 035 $a(SSID)ssj0001482330 035 $a(PQKBManifestationID)12496229 035 $a(PQKBTitleCode)TC0001482330 035 $a(PQKBWorkID)11508592 035 $a(PQKB)10943492 035 $a(DE-B1597)444958 035 $a(OCoLC)912323205 035 $a(DE-B1597)9781501501500 035 $a(MiAaPQ)EBC1820373 035 $a(Au-PeEL)EBL1820373 035 $a(CaPaEBR)ebr11059834 035 $a(CaONFJC)MIL808157 035 $a(OCoLC)910408036 035 $a(EXLCZ)993710000000420347 100 $a20150609h20152015 uy 0 101 0 $aeng 135 $aur|nu---|u||u 181 $ctxt 182 $cc 183 $acr 200 10$aMachine learning for protein subcellular localization prediction /$fShibiao Wan, Man-Wai Mak 210 1$aBerlin, Germany ;$aBoston, Massachusetts :$cDe Gruyter,$d2015. 210 4$dİ2015 215 $a1 online resource (210 p.) 300 $aDescription based upon print version of record. 311 $a1-5015-1048-7 320 $aIncludes bibliographical references and index. 327 $tFront matter --$tPreface --$tContents --$tList of Abbreviations --$t1. Introduction --$t2. Overview of subcellular localization prediction --$t3. Legitimacy of using gene ontology information --$t4. Single-location protein subcellular localization --$t5. From single- to multi-location --$t6. Mining deeper on GO for protein subcellular localization --$t7. Ensemble random projection for large-scale predictions --$t8. Experimental setup --$t9. Results and analysis --$t10. Properties of the proposed predictors --$t11. Conclusions and future directions --$tA. Webservers for protein subcellular localization --$tB. Support vector machines --$tC. Proof of no bias in LOOCV --$tD. Derivatives for penalized logistic regression --$tBibliography --$tIndex 330 $aComprehensively 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. 606 $aProteins$xPhysiological transport$xData processing 606 $aMachine learning 606 $aProbabilities$xData processing 608 $aElectronic books. 615 0$aProteins$xPhysiological transport$xData processing. 615 0$aMachine learning. 615 0$aProbabilities$xData processing. 676 $a572/.696 686 $aWC 7700$2rvk 700 $aWan$b Shibiao$01049324 702 $aMak$b M. W. 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910460442103321 996 $aMachine learning for protein subcellular localization prediction$92478224 997 $aUNINA LEADER 01426nam 2200373Ia 450 001 996389062403316 005 20221108000757.0 035 $a(CKB)4940000000094940 035 $a(EEBO)2240867436 035 $a(OCoLC)69648848 035 $a(EXLCZ)994940000000094940 100 $a20060530d1696 uy 0 101 0 $aeng 135 $aurbn||||a|bb| 200 10$aReflections upon the devotions of the Roman Church$b[electronic resource] $ewith the prayers, hymns and lessions themselves, taken out of their authentick books /$fby John Patrick 205 $aThe third edition, with an appendix concerning the miracles and reliques of the Church of Rome. 210 $aLondon $cPrinted for Richard Cumberland, at the Angel in St. Paul's Church-Yard$d1696 215 $a[12], 434, 6, [14] p 300 $aIncludes index. 300 $aReproduction of original in: Peterborough Cathedral. 330 $aeebo-0124 606 $aDevotional literature, English$vEarly works to 1800 606 $aMiracles$vEarly works to 1800 606 $aTheology, Doctrinal$vEarly works to 1800 615 0$aDevotional literature, English 615 0$aMiracles 615 0$aTheology, Doctrinal 700 $aPatrick$b John$f1632-1695.$01002735 801 0$bUMI 801 1$bUMI 906 $aBOOK 912 $a996389062403316 996 $aReflections upon the devotions of the Roman Church$92373190 997 $aUNISA