LEADER 00870nam0-22003011i-450- 001 990001319850403321 010 $a0-534-09402-3 035 $a000131985 035 $aFED01000131985 035 $a(Aleph)000131985FED01 035 $a000131985 100 $a20000920d1989----km-y0itay50------ba 101 0 $aeng 200 1 $aIntroduction to computer science using Pascal$fJoao P. Martins. 210 $aBelmont$cWadsworth$dc1989 215 $axxii, 537 p.$d24 cm 610 0 $aComputer science$aProgrammazione 610 0 $aLinguaggi di programmazione$aPascal 676 $a005.13 700 1$aMartins,$bJoao P.$059793 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990001319850403321 952 $a124-A-12$b10600$fMA1 959 $aMA1 996 $aIntroduction to computer science using Pascal$9383082 997 $aUNINA DB $aING01 LEADER 05354nam 2200721Ia 450 001 9910140840803321 005 20200520144314.0 010 $a9786612707575 010 $a9781282707573 010 $a1282707574 010 $a9780470593417 010 $a0470593415 010 $a9780470593400 010 $a0470593407 035 $a(CKB)2670000000035335 035 $a(EBL)565027 035 $a(OCoLC)662453072 035 $a(SSID)ssj0000415448 035 $a(PQKBManifestationID)11322632 035 $a(PQKBTitleCode)TC0000415448 035 $a(PQKBWorkID)10411033 035 $a(PQKB)10996047 035 $a(MiAaPQ)EBC565027 035 $a(PPN)185060501 035 $a(Perlego)2756979 035 $a(EXLCZ)992670000000035335 100 $a20100104d2010 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aData mining for genomics and proteomics $eanalysis of gene and protein expression data /$fDarius M. Dzuida 210 $aHoboken, N.J. $cWiley$dc2010 215 $a1 online resource (348 p.) 225 1 $aWiley Series on Methods and Applications in Data Mining ;$vv.1 300 $aDescription based upon print version of record. 311 08$a9780470163733 311 08$a0470163739 320 $aIncludes bibliographical references and index. 327 $aDATA MINING FOR GENOMICS AND PROTEOMICS; CONTENTS; PREFACE; ACKNOWLEDGMENTS; 1 INTRODUCTION; 1.1 Basic Terminology; 1.1.1 The Central Dogma of Molecular Biology; 1.1.2 Genome; 1.1.3 Proteome; 1.1.4 DNA (Deoxyribonucleic Acid); 1.1.5 RNA (Ribonucleic Acid); 1.1.6 mRNA (messenger RNA); 1.1.7 Genetic Code; 1.1.8 Gene; 1.1.9 Gene Expression and the Gene Expression Level; 1.1.10 Protein; 1.2 Overlapping Areas of Research; 1.2.1 Genomics; 1.2.2 Proteomics; 1.2.3 Bioinformatics; 1.2.4 Transcriptomics and Other -omics . . .; 1.2.5 Data Mining; 2 BASIC ANALYSIS OF GENE EXPRESSION MICROARRAY DATA 327 $a2.1 Introduction2.2 Microarray Technology; 2.2.1 Spotted Microarrays; 2.2.2 Affymetrix GeneChip(®) Microarrays; 2.2.3 Bead-Based Microarrays; 2.3 Low-Level Preprocessing of Affymetrix Microarrays; 2.3.1 MAS5; 2.3.2 RMA; 2.3.3 GCRMA; 2.3.4 PLIER; 2.4 Public Repositories of Microarray Data; 2.4.1 Microarray Gene Expression Data Society (MGED) Standards; 2.4.2 Public Databases; 2.4.2.1 Gene Expression Omnibus (GEO); 2.4.2.2 ArrayExpress; 2.5 Gene Expression Matrix; 2.5.1 Elements of Gene Expression Microarray Data Analysis; 2.6 Additional Preprocessing, Quality Assessment, and Filtering 327 $a2.6.1 Quality Assessment2.6.2 Filtering; 2.7 Basic Exploratory Data Analysis; 2.7.1 t Test; 2.7.1.1 t Test for Equal Variances; 2.7.1.2 t Test for Unequal Variances; 2.7.2 ANOVA F Test; 2.7.3 SAM t Statistic; 2.7.4 Limma; 2.7.5 Adjustment for Multiple Comparisons; 2.7.5.1 Single-Step Bonferroni Procedure; 2.7.5.2 Single-Step Sidak Procedure; 2.7.5.3 Step-Down Holm Procedure; 2.7.5.4 Step-Up Benjamini and Hochberg Procedure; 2.7.5.5 Permutation Based Multiplicity Adjustment; 2.8 Unsupervised Learning (Taxonomy-Related Analysis); 2.8.1 Cluster Analysis 327 $a2.8.1.1 Measures of Similarity or Distance2.8.1.2 K-Means Clustering; 2.8.1.3 Hierarchical Clustering; 2.8.1.4 Two-Way Clustering and Related Methods; 2.8.2 Principal Component Analysis; 2.8.3 Self-Organizing Maps; Exercises; 3 BIOMARKER DISCOVERY AND CLASSIFICATION; 3.1 Overview; 3.1.1 Gene Expression Matrix . . . Again; 3.1.2 Biomarker Discovery; 3.1.3 Classification Systems; 3.1.3.1 Parametric and Nonparametric Learning Algorithms; 3.1.3.2 Terms Associated with Common Assumptions Underlying Parametric Learning Algorithms; 3.1.3.3 Visualization of Classification Results 327 $a3.1.4 Validation of the Classification Model3.1.4.1 Reclassification; 3.1.4.2 Leave-One-Out and K-Fold Cross-Validation; 3.1.4.3 External and Internal Cross-Validation; 3.1.4.4 Holdout Method of Validation; 3.1.4.5 Ensemble-Based Validation (Using Out-of-Bag Samples); 3.1.4.6 Validation on an Independent Data Set; 3.1.5 Reporting Validation Results; 3.1.5.1 Binary Classifiers; 3.1.5.2 Multiclass Classifiers; 3.1.6 Identifying Biological Processes Underlying the Class Differentiation; 3.2 Feature Selection; 3.2.1 Introduction; 3.2.2 Univariate Versus Multivariate Approaches 327 $a3.2.3 Supervised Versus Unsupervised Methods 330 $aData Mining for Genomics and Proteomics uses pragmatic examples and a complete case study to demonstrate step-by-step how biomedical studies can be used to maximize the chance of extracting new and useful biomedical knowledge from data. It is an excellent resource for students and professionals involved with gene or protein expression data in a variety of settings. 410 0$aWiley Series on Methods and Applications in Data Mining 606 $aGenomics$xData processing 606 $aProteomics$xData processing 606 $aData mining 615 0$aGenomics$xData processing. 615 0$aProteomics$xData processing. 615 0$aData mining. 676 $a572.8/6 700 $aDziuda$b Darius M$0511013 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910140840803321 996 $aData mining for genomics and proteomics$9768407 997 $aUNINA