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 01755nam 2200409 n 450 001 996392296003316 005 20200824121822.0 035 $a(CKB)4940000000109204 035 $a(EEBO)2240903750 035 $a(UnM)99864688e 035 $a(UnM)99864688 035 $a(EXLCZ)994940000000109204 100 $a19931207d1648 uy | 101 0 $aeng 135 $aurbn||||a|bb| 200 00$aPrince Charles his declaration, commended to the publique, for the satisfaction of all His Majesties loyall subjects$b[electronic resource] $eWith his letter to Sir Marmaduke Langdale, and Sir Thomas Glemham, relating thereunto. Together with their letter from the printing thereof 210 $a[London $cs.n.]$dAnno, 1648 215 $a[8] p 300 $aSignatures: [A]?. 300 $aPlace of publication from Wing. 300 $aSigned on p.[8]: C.P. (Prince Charles), M.L. (i.e. Marmaduke Langdale), and T.G. (i.e. Thomas Glemham). 300 $aAnnotation on Thomason copy: "July 7th". 300 $aIn this edition, the printers device on the titlepage consists of four squares. 300 $aReproduction of the original in the British Library. 330 $aeebo-0018 607 $aGreat Britain$xHistory$yCivil War, 1642-1649$vEarly works to 1800 701 $aCharles$cKing of England,$f1630-1685.$0793293 701 $aGlemham$b Thomas$cSir,$fd. 1649.$01000884 701 $aLangdale$b Marmaduke$cSir,$f1598?-1661.$01013157 801 0$bCu-RivES 801 1$bCu-RivES 801 2$bCStRLIN 801 2$bWaOLN 906 $aBOOK 912 $a996392296003316 996 $aPrince Charles his declaration, commended to the publique, for the satisfaction of all His Majesties loyall subjects$92355069 997 $aUNISA