LEADER 04014nam 2200637 a 450 001 9910145957003321 005 20230721005143.0 010 $a1-282-03070-1 010 $a9786612030703 010 $a0-470-39742-X 010 $a0-470-39741-1 035 $a(CKB)1000000000719486 035 $a(EBL)427638 035 $a(SSID)ssj0000195100 035 $a(PQKBManifestationID)11178701 035 $a(PQKBTitleCode)TC0000195100 035 $a(PQKBWorkID)10241797 035 $a(PQKB)11282379 035 $a(Au-PeEL)EBL427638 035 $a(CaPaEBR)ebr10296669 035 $a(CaONFJC)MIL203070 035 $a(OCoLC)352745595 035 $a(MiAaPQ)EBC427638 035 $a(EXLCZ)991000000000719486 100 $a20080415d2009 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aMachine learning in bioinformatics$b[electronic resource] /$fedited by Yan-Qing Zhang, Jagath C. Rajapakse 210 $aHoboken, N.J. $cWiley$dc2009 215 $a1 online resource (476 p.) 225 1 $aWiley series on bioinformatics 300 $aDescription based upon print version of record. 311 $a0-470-11662-5 320 $aIncludes bibliographical references and index. 327 $aMACHINE LEARNING IN BIOINFORMATICS; CONTENTS; Foreword; Preface; Contributors; 1 Feature Selection for Genomic and Proteomic Data Mining; 2 Comparing and Visualizing Gene Selection and Classification Methods for Microarray Data; 3 Adaptive Kernel Classifiers Via Matrix Decomposition Updating for Biological Data Analysis; 4 Bootstrapping Consistency Method for Optimal Gene Selection from Microarray Gene Expression Data for Classification Problems; 5 Fuzzy Gene Mining: A Fuzzy-Based Framework for Cancer Microarray Data Analysis; 6 Feature Selection for Ensemble Learning and Its Application 327 $a7 Sequence-Based Prediction of Residue-Level Properties in Proteins8 Consensus Approaches to Protein Structure Prediction; 9 Kernel Methods in Protein Structure Prediction; 10 Evolutionary Granular Kernel Trees for Protein Subcellular Location Prediction; 11 Probabilistic Models for Long-Range Features in Biosequences; 12 Neighborhood Profile Search for Motif Refinement; 13 Markov/Neural Model for Eukaryotic Promoter Recognition; 14 Eukaryotic Promoter Detection Based on Word and Sequence Feature Selection and Combination 327 $a15 Feature Characterization and Testing of Bidirectional Promoters in the Human Genome-Significance and Applications in Human Genome Research16 Supervised Learning Methods for MicroRNA Studies; 17 Machine Learning for Computational Haplotype Analysis; 18 Machine Learning Applications in SNP-Disease Association Study; 19 Nanopore Cheminformatics-Based Studies of Individual Molecular Interactions; 20 An Information Fusion Framework for Biomedical Informatics; Index 330 $aAn introduction to machine learning methods and their applications to problems in bioinformatics Machine learning techniques are increasingly being used to address problems in computational biology and bioinformatics. Novel computational techniques to analyze high throughput data in the form of sequences, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery. Machine learning techniques such as Markov models, support vector machines, neural networks, and graphical models have been successful in analyzing life science data b 410 0$aWiley series on bioinformatics. 606 $aBioinformatics 606 $aMachine learning 615 0$aBioinformatics. 615 0$aMachine learning. 676 $a572.80285/61 700 $aZhang$b Yan-Qing$0902087 701 $aRajapakse$b Jagath Chandana$0961203 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910145957003321 996 $aMachine learning in bioinformatics$92179119 997 $aUNINA