LEADER 01946nlm--2200433---450 001 990003693720203316 005 20201218111045.0 010 $a978-1-4160-3000-3 035 $a000369372 035 $aUSA01000369372 035 $a(ALEPH)000369372USA01 035 $a000369372 100 $a20121002d2008----km-y0itay50------ba 101 $aeng 102 $aUS 105 $a||||||||001yy 135 $adrcnu-------- 200 1 $aClinical men?s health: evidence in practice$fedited by Joel J. Heidelbaugh, Eric R.M. Jauniaux, Mark B. Landon$bRisorsa elettronica 210 $aPhiladelphia$cSaunders/Elsevier$d2008 215 $aTesto elettronico (PDF) (XII, 608 p.)$cill. 230 $aBase dati testuale 330 $aHere's the first evidence-based guide to focus solely on the various health conditions that unequally affect men. This text provides a biopsychosocial approach to diseases and disorders of male patients from birth through infanthood, childhood, and adolescence, and from early through late adulthood. Replete with current evidence-based guidelines to facilitate clinical decision-making, the framework of each chapter builds upon epidemiological data centered on men. Special attention is given to the circumstances that influence men to either seek or not seek routine medical care. 410 0$12001 454 1$12001 461 1$1001-------$12001 606 0 $aUomini$xSalute$2BNCF 676 $a613.04234 700 1$aHEIDELBAUGH,$bJoel J.$0613002 701 1$aJAUNIAUX,$bEric R.M.$0613003 701 1$aLANDON,$bMark B.$0613004 801 0$aIT$bsalbc$gISBD 856 4 $uhttp://www.sciencedirect.com/science/book/ 9781416030003$zAccesso limitato alla rete di Ateneo$4. 912 $a990003693720203316 959 $aEB 969 $aER 969 $aMED 979 $aFIORELLA$b90$c20121002$lUSA01$h1045 996 $aClinical men?s health: evidence in practice$91142048 997 $aUNISA LEADER 03230nam 2200709 450 001 9910819391103321 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 $a9910819391103321 996 $aMachine learning for protein subcellular localization prediction$94049568 997 $aUNINA