LEADER 00786nam0-22002771i-450- 001 990004632060403321 005 19990530 035 $a000463206 035 $aFED01000463206 035 $a(Aleph)000463206FED01 035 $a000463206 100 $a19990530d1856----km-y0itay50------ba 101 0 $aita 105 $ay-------001yy 200 1 $aEpistolario$fdi Silvio Pellico$graccolto e pubblicato per cura di Guglielmo Stefani 210 $aFirenze$cLe Monnier$d1856. 215 $aIV, 475 p.$d18 cm 700 1$aPellico,$bSilvio$f<1789?1854> 702 1$aStefani,$bGuglielmo 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990004632060403321 952 $aCOLL.126(107)$bBibl.6112$fFLFBC 959 $aFLFBC 996 $aEpistolario$9131249 997 $aUNINA LEADER 05253nam 2200649 a 450 001 9910831180403321 005 20230721025913.0 010 $a1-280-90018-0 010 $a9786610900183 010 $a0-470-12464-4 010 $a0-470-12463-6 035 $a(CKB)1000000000354677 035 $a(EBL)297259 035 $a(SSID)ssj0000187901 035 $a(PQKBManifestationID)11180619 035 $a(PQKBTitleCode)TC0000187901 035 $a(PQKBWorkID)10137970 035 $a(PQKB)11695664 035 $a(MiAaPQ)EBC297259 035 $a(OCoLC)181344797 035 $a(EXLCZ)991000000000354677 100 $a20060929d2007 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aKnowledge discovery in bioinformatics$b[electronic resource] $etechniques, methods, and applications /$fedited by Xiaohua Hu, Yi Pan 210 $aHoboken, N.J. $cWiley-Interscience$dc2007 215 $a1 online resource (405 p.) 225 1 $aWiley Series in Bioinformatics 300 $aDescription based upon print version of record. 311 $a0-471-77796-X 327 $aKNOWLEDGE DISCOVERY IN BIOINFORMATICS; CONTENTS; Contributors; Preface; 1 Current Methods for Protein Secondary-Structure Prediction Based on Support Vector Machines; 1.1 Traditional Methods; 1.1.1 Statistical Approaches; 1.1.2 Machine Learning Approaches; 1.2 Support Vector Machine Method; 1.2.1 Introduction to SVM; 1.2.2 Encoding Profile; 1.2.3 Kernel Functions; 1.2.4 Tertiary Classifier Design; 1.2.5 Accuracy Measure of SVM; 1.3 Performance Comparison of SVM Methods; 1.4 Discussion and Conclusions; References; 2 Comparison of Seven Methods for Mining Hidden Links 327 $a2.1 Analysis of the Literature on Raynaud's Disease2.2 Related Work; 2.3 Methods; 2.3.1 Information Measures; 2.3.2 Ranking Methods; 2.3.3 Seven Methods; 2.4 Experiment Results and Analysis; 2.4.1 Data Set; 2.4.2 Chi-Square, Chi-Square Association Rule, and Mutual Information Link ABC Methods Compared; 2.4.3 Chi-Square ABC Method: Semantic Check for Mining Implicit Connections; 2.4.4 Chi-Square and Mutual Information Link ABC Methods; 2.5 Discussion and Conclusions; Acknowledgments; References; 3 Voting Scheme-Based Evolutionary Kernel Machines for Drug Activity Comparisons 327 $a3.1 Granular Kernel and Kernel Tree Design3.1.1 Definitions; 3.1.2 Granular Kernel Properties; 3.2 GKTSESs; 3.3 Evolutionary Voting Kernel Machines; 3.4 Simulations; 3.4.1 Data Set and Experimental Setup; 3.4.2 Experimental Results and Comparisons; 3.5 Conclusions and Future Work; Acknowledgments; References; 4 Bioinformatics Analyses of Arabidopsis thaliana Tiling Array Expression Data; 4.1 Tiling Array Design and Data Description; 4.1.1 Data; 4.1.2 Tiling Array Expression Patterns; 4.1.3 Tiling Array Data Analysis; 4.2 Ontology Analyses; 4.3 Antisense Regulation Identification 327 $a4.3.1 Antisense Silencing4.3.2 Antisense Regulation Identification; 4.4 Correlated Expression Between Two DNA Strands; 4.5 Identification of Nonprotein Coding mRNA; 4.6 Summary; Acknowledgments; References; 5 Identification of Marker Genes from High-Dimensional Microarray Data for Cancer Classification; 5.1 Feature Selection; 5.1.1 Taxonomy of Feature Selection; 5.1.2 Evaluation Criterion; 5.1.3 Generation Procedure; 5.2 Gene Selection; 5.2.1 Individual Gene Ranking; 5.2.2 Gene Subset Selection; 5.2.3 Summary of Gene Selection; 5.3 Comparative Study of Gene Selection Methods 327 $a5.3.1 Microarray Data Descriptions5.3.2 Gene Selection Approaches; 5.3.3 Experimental Results; 5.4 Conclusions and Discussion; Acknowledgments; References; 6 Patient Survival Prediction from Gene Expression Data; 6.1 General Methods; 6.1.1 Kaplan-Meier Survival Analysis; 6.1.2 Cox Proportional-Hazards Regression; 6.2 Applications; 6.2.1 Diffuse Large-B-Cell Lymphoma; 6.2.2 Lung Adenocarcinoma; 6.2.3 Remarks; 6.3 Incorporating Data Mining Techniques to Survival Prediction; 6.3.1 Gene Selection by Statistical Properties; 6.3.2 Cancer Subtype Identification via Survival Information 327 $a6.4 Selection of Extreme Patient Samples 330 $aThe purpose of this edited book is to bring together the ideas and findings of data mining researchers and bioinformaticians by discussing cutting-edge research topics such as, gene expressions, protein/RNA structure prediction, phylogenetics, sequence and structural motifs, genomics and proteomics, gene findings, drug design, RNAi and microRNA analysis, text mining in bioinformatics, modelling of biochemical pathways, biomedical ontologies, system biology and pathways, and biological database management. 410 0$aWiley Series in Bioinformatics 606 $aBioinformatics 606 $aComputational biology 615 0$aBioinformatics. 615 0$aComputational biology. 676 $a570.285 676 $a570/.285 676 $a572.80285 701 $aHu$b Xiaohua$f1960-$01675128 701 $aPan$b Yi$f1960-$01646467 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910831180403321 996 $aKnowledge discovery in bioinformatics$94040391 997 $aUNINA LEADER 01116nam a2200277 i 4500 001 991000479599707536 005 20260115140722.0 008 980611s1993 it ||| | ita 020 $a8815040633 035 $ab10083613-39ule_inst 035 $aLE02516793$9ExL 040 $aBibl. Dip.le Aggr. Scienze Economia - Sez. Settore Economico 082 04$a330.1 100 1 $aPasinetti, Luigi$0460494 245 10$aDinamica economica strutturale :$bun'indagine teorica sulle conseguenze economiche dell'apprendimento umano /$cLuigi Pasinetti 260 $aBologna :$bIl mulino,$cc1993 300 $a294 p. ;$c22 cm 490 0 $aRicerca 650 4$aEconomia$xTeorie 907 $a.b10083613$b17-02-17$c27-06-02 912 $a991000479599707536 945 $aLE025 ECO 330.1 PAS01.01$g1$i2025000044876$lle025$o-$pE0.00$q-$rl$s-$t0$u0$v0$w0$x0$y.i10095895$z27-06-02 945 $aLE025 ECO 330.1 PAS01.01$g2$i2025000195738$lle025$o-$pE0.00$q-$rl$s-$t0$u0$v0$w0$x0$y.i14671852$z15-02-08 996 $aDinamica economica strutturale$9195782 997 $aUNISALENTO 998 $ale025$b01-01-98$cm$da$e-$fita$git$h0$i1