LEADER 04603nam 2200601 450 001 9910144107303321 005 20180731043452.0 010 $a1-281-94703-2 010 $a9786611947033 010 $a3-527-62281-0 010 $a3-527-62282-9 035 $a(CKB)1000000000552946 035 $a(EBL)481603 035 $a(OCoLC)264703283 035 $a(SSID)ssj0000102958 035 $a(PQKBManifestationID)11120115 035 $a(PQKBTitleCode)TC0000102958 035 $a(PQKBWorkID)10061178 035 $a(PQKB)11560498 035 $a(MiAaPQ)EBC481603 035 $a(EXLCZ)991000000000552946 100 $a20160818h20082008 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aAnalysis of microarray data $ea network-based approach /$fedited by Frank Emmert-Streib and Matthias Dehmer 210 1$aWeinheim, [Germany] :$cWiley-VCH Verlag GmbH & Co. KGaA,$d2008. 210 4$dİ2008 215 $a1 online resource (440 p.) 300 $aDescription based upon print version of record. 311 $a3-527-31822-4 320 $aIncludes bibliographical references at the end of each chapters and index. 327 $aAnalysis of Microarray Data; Contents; Preface; List of Contributors; 1 Introduction to DNA Microarrays; 1.1 Introduction; 1.1.1 The Genome is an Information Scaffold; 1.1.2 Gene Expression is Detected by Hybridization; 1.1.2.1 Hybridization is Used to Measure Gene Expression; 1.1.2.2 Microarrays Provide a New Twist to an Old Technique; 1.2 Types of Arrays; 1.2.1 Spotted Microarrays; 1.2.2 Affymetrix GeneChips; 1.2.2.1 Other In Situ Synthesis Platforms; 1.2.2.2 Uses of Microarrays; 1.3 Array Content; 1.3.1 ESTs Are the First View; 1.3.1.1 Probe Design; 1.4 Normalization and Scaling 327 $a1.4.1 Be Unbiased, Be Complete1.4.2 Sequence Counts; References; 2 Comparative Analysis of Clustering Methods for Microarray Data; 2.1 Introduction; 2.2 Measuring Distance Between Genes or Clusters; 2.3 Network Models; 2.3.1 Boolean Network; 2.3.2 Coexpression Network; 2.3.3 Bayesian Network; 2.3.4 Co-Occurrence Network; 2.4 Network Constrained Clustering Method; 2.4.1 Extract the Giant Connected Component; 2.4.2 Compute "Network Constrained Distance Matrix"; 2.5 Network Constrained Clustering Results; 2.5.1 Yeast Galactose Metabolism Pathway; 2.5.2 Retinal Gene Expression Data 327 $a2.5.3 Mouse Segmentation Clock Data2.6 Discussion and Conclusion; References; 3 Finding Verified Edges in Genetic/Gene Networks: Bilayer Verification for Network Recovery in the Presence of Hidden Confounders; 3.1 Introduction: Gene and Genetic Networks; 3.2 Background and Prior Theory; 3.2.1 Motivation; 3.2.2 Bayesian Networks Theory; 3.2.2.1 d-Separation at Colliders; 3.2.2.2 Placing Genetic Tests Within the Bayesian Network Framework; 3.2.3 Learning Network Structure from Observed Conditional Independencies; 3.2.4 Prior Work: The PC Algorithm; 3.2.4.1 PC Algorithm 327 $a3.5 Results and Further Application3.5.1 Estimating ? False-Positive Rates for the v-Structure Test; 3.5.2 Learning an Aortic Lesion Network; 3.5.3 Further Utilizing Networks: Assigning Functional Roles to Genes; 3.5.4 Future Work; References; 4 Computational Inference of Biological Causal Networks - Analysis of Therapeutic Compound Effects; 4.1 Introduction; 4.2 Basic Theory of Bayesian Networks; 4.2.1 Bayesian Scoring Metrics; 4.2.2 Heuristic Search Methods; 4.2.3 Inference Score; 4.3 Methods; 4.3.1 Experimental Design; 4.3.2 Tissue Contamination; 4.3.3 Gene List Prefiltering 327 $a4.3.4 Outlier Removal 330 $aThis book is the first to focus on the application of mathematical networks for analyzing microarray data. This method goes well beyond the standard clustering methods traditionally used. From the contents:* Understanding and Preprocessing Microarray Data* Clustering of Microarray Data* Reconstruction of the Yeast Cell Cycle by Partial Correlations of Higher Order* Bilayer Verification Algorithm* Probabilistic Boolean Networks as Models for Gene Regulation* Estimating Transcriptional Regulatory Networks by a Bayesian Network* Analysis of Therapeutic Compound Eff 606 $aDNA microarrays 608 $aElectronic books. 615 0$aDNA microarrays. 676 $a572.8636 702 $aEmmert-Streib$b Frank 702 $aDehmer$b Matthias 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910144107303321 996 $aAnalysis of microarray data$92181928 997 $aUNINA