LEADER 01059nam a2200289 i 4500 001 991001441649707536 005 20020507193522.0 008 960522s1979 it ||| | ita 035 $ab10848009-39ule_inst 035 $aLE01312320$9ExL 040 $aDip.to Matematica$beng 082 0 $a516.352 084 $aAMS 14J 100 1 $aAcquistapace, F.$0537223 245 10$aTipologia delle superfici algebriche in P3(C) /$cF. Acquistapace, F. Broglia, F. Lazzeri 260 $aBologna :$bPitagora,$c1979 300 $a171 p. ;$c25 cm. 490 0 $aQuaderni dell'Unione Matematica Italiana ;$v13 650 4$aSurfaces and higher-dimensional varieties 700 1 $aBroglia, Fabrizio 700 1 $aLazzeri, F. 907 $a.b10848009$b23-02-17$c28-06-02 912 $a991001441649707536 945 $aLE013 14J ACQ11 (1979)$g1$i2013000048482$lle013$o-$pE0.00$q-$rl$s- $t0$u1$v0$w1$x0$y.i10958976$z28-06-02 996 $aTipologia delle superfici algebriche in P3(C$9918568 997 $aUNISALENTO 998 $ale013$b01-01-96$cm$da $e-$fita$git $h0$i1 LEADER 03230nam 2200709 450 001 9910797139603321 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(Au-PeEL)EBL1820373 035 $a(CaPaEBR)ebr11059834 035 $a(CaONFJC)MIL808157 035 $a(OCoLC)910408036 035 $a(CaSebORM)9781501501524 035 $a(MiAaPQ)EBC1820373 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 610 $aBioinformatics. 610 $aComputer Science. 610 $aProteomics. 615 0$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$01128379 702 $aMak$b M. W. 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910797139603321 996 $aMachine learning for protein subcellular localization prediction$93751463 997 $aUNINA