LEADER 01410nam0 2200349 450 001 000018446 005 20050920160953.0 010 $a1-58488-451-7 100 $a20050915d2004----km-y0itaa50------ba 101 0 $aeng 102 $aUS 200 1 $aTrasform method for solving partial differential equations$fDean G. Duffy 205 $a2. ed. 210 $aBoca Raton...[etc.]$cChapman and Hall/CRC$d2004 215 $aXI, 708 p.$d24 cm. 606 1 $aTrasformazioni di Fourier 606 1 $aEquazioni alle derivate parziali 676 $a515.353$v(21. ed.)$9Equazioni differenziali parziali 700 1$aDuffy,$bDean G.$041450 801 0$aIT$bUniversità degli Studi della Basilicata$gRICA$2unimarc 912 $a000018446 996 $aTrasform method for solving partial differential equations$984294 997 $aUNIBAS CAT $aTTM$b30$c20050915$lBAS01$h1358 CAT $aTTM$b30$c20050915$lBAS01$h1408 CAT $aTTM$b30$c20050915$lBAS01$h1410 CAT $aTTM$b30$c20050915$lBAS01$h1426 CAT $aFCL$b01$c20050920$lBAS01$h1525 CAT $aFCL$b01$c20050920$lBAS01$h1609 FMT Z30 -1$lBAS01$LBAS01$mBOOK$1BASA2$APolo Tecnico-Scientifico$2DID$BDidattica$3PTS.s1.p26.7$6103542$5T103542$820050915$f98$FConsultazione Z30 -1$lBAS01$LBAS01$mBOOK$1BASA2$APolo Tecnico-Scientifico$2DID$BDidattica$3PTS.s1.p26.7A$6103543$5T103543$820050915$f04$FPrestabile Didattica 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