LEADER 00988nam0-22002771i-450- 001 990000879320403321 005 20040618115711.0 035 $a000087932 035 $aFED01000087932 035 $a(Aleph)000087932FED01 035 $a000087932 100 $a20020821d1932----km-y0itay50------ba 101 0 $aeng 105 $ay-------001yy 200 1 $aFirst congress Internatiol Association for Bridge and Structural Engineering Paris, 19 mai-25 mai 1932$epreliminari publication$fIABSE 210 $aParis$cAssociation Int. Ponts Charpentes$d1932 215 $a683 p.$cill.$d24 cm 610 0 $aElementi strutturali 710 02$aInternational association for bridge and structural engineering$024566 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990000879320403321 952 $a03 ES.0,75$b60$fIINTC 959 $aIINTC 996 $aFirst congress Internatiol Association for Bridge and Structural Engineering Paris, 19 mai-25 mai 1932$9355765 997 $aUNINA LEADER 01668nam--2200517---450- 001 990000374380203316 005 20051021102615.0 010 $a0674995635 035 $a0037438 035 $aUSA010037438 035 $a(ALEPH)000037438USA01 035 $a0037438 100 $a20010326h19951999km-y0itay0103----ba 101 $aeng 102 $aGB 105 $a||||||||001yy 200 1 $aPoetics$fAristotle$gedited and translated by Stephen Halliwell$aOn the sublime$fLonginus$gtranslation by W.H. Fyfe$grevised by Donald Russel$aOn style$fDemetrius$gedited and translated by Doreen C.Innes$gbased on W.Rhys Roberts 210 $aCambridge$cHarvard University$d1995$h1999 215 $a533 p.$d17 cm 225 2 $aLoeb classical library$v199 300 $aTesto orig. a fronte 410 $12001$aLoeb classical library$v199 461 1$1001-------$12001 676 $a185 700 1$aARISTOTELES$04207 701 1$aLONGINUS$0330739 701 1$aDEMETRIUS$0171847 702 1$aHALLIWELL,$bStephen 702 1$aFYFE,$bW.H. 702 1$aINNES,$bDoreen C. 702 1$aRUSSEL,$bDonald 702 1$aROBERTS,$bRhys 801 0$aIT$bsalbc$gISBD 912 $a990000374380203316 951 $aV.1. Coll.7/ 41/23(VIII A 972 ARI/23)$b149092 LM$cVIII A 959 $aBK 969 $aUMA 979 $aPATTY$b90$c20010326$lUSA01$h1430 979 $aPATTY$b90$c20010326$lUSA01$h1435 979 $c20020403$lUSA01$h1645 979 $aPATRY$b90$c20040406$lUSA01$h1626 979 $aCOPAT3$b90$c20051021$lUSA01$h1026 996 $aPoetics$9877677 996 $aOn style$9877679 996 $aOn the sublime$9877678 997 $aUNISA 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