LEADER 05735nam 22007575 450 001 9910298568303321 005 20200703055841.0 010 $a3-319-05630-1 024 7 $a10.1007/978-3-319-05630-2 035 $a(CKB)2670000000548001 035 $a(EBL)1698356 035 $a(OCoLC)874855286 035 $a(SSID)ssj0001186562 035 $a(PQKBManifestationID)11674064 035 $a(PQKBTitleCode)TC0001186562 035 $a(PQKBWorkID)11240357 035 $a(PQKB)11459686 035 $a(MiAaPQ)EBC1698356 035 $a(DE-He213)978-3-319-05630-2 035 $a(PPN)177822554 035 $a(EXLCZ)992670000000548001 100 $a20140319d2014 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aScalable Pattern Recognition Algorithms $eApplications in Computational Biology and Bioinformatics /$fby Pradipta Maji, Sushmita Paul 205 $a1st ed. 2014. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2014. 215 $a1 online resource (316 p.) 300 $aDescription based upon print version of record. 311 $a3-319-05629-8 320 $aIncludes bibliographical references at the end of each chapters and index. 327 $aIntroduction to Pattern Recognition and Bioinformatics -- Part I Classification -- Neural Network Tree for Identification of Splice Junction and Protein Coding Region in DNA -- Design of String Kernel to Predict Protein Functional Sites Using Kernel-Based Classifiers -- Part II Feature Selection -- Rough Sets for Selection of Molecular Descriptors to Predict Biological Activity of Molecules -- f -Information Measures for Selection of Discriminative Genes from Microarray Data -- Identification of Disease Genes Using Gene Expression and Protein-Protein Interaction Data -- Rough Sets for Insilico Identification of Differentially Expressed miRNAs -- Part III Clustering -- Grouping Functionally Similar Genes from Microarray Data Using Rough-Fuzzy Clustering -- Mutual Information Based Supervised Attribute Clustering for Microarray Sample Classification -- Possibilistic Biclustering for Discovering Value-Coherent Overlapping d -Biclusters -- Fuzzy Measures and Weighted Co-Occurrence Matrix for Segmentation of Brain MR Images. 330 $aRecent advances in high-throughput technologies have resulted in a deluge of biological information. Yet the storage, analysis, and interpretation of such multifaceted data require effective and efficient computational tools. This unique text/reference addresses the need for a unified framework describing how soft computing and machine learning techniques can be judiciously formulated and used in building efficient pattern recognition models. The book reviews both established and cutting-edge research, following a clear structure reflecting the major phases of a pattern recognition system: classification, feature selection, and clustering. The text provides a careful balance of theory, algorithms, and applications, with a particular emphasis given to applications in computational biology and bioinformatics. Topics and features: Reviews the development of scalable pattern recognition algorithms for computational biology and bioinformatics Integrates different soft computing and machine learning methodologies with pattern recognition tasks Discusses in detail the integration of different techniques for handling uncertainties in decision-making and efficiently mining large biological datasets Presents a particular emphasis on real-life applications, such as microarray expression datasets and magnetic resonance images Includes numerous examples and experimental results to support the theoretical concepts described Concludes each chapter with directions for future research and a comprehensive bibliography This important work will be of great use to graduate students and researchers in the fields of computer science, electrical and biomedical engineering. Researchers and practitioners involved in pattern recognition, machine learning, computational biology and bioinformatics, data mining, and soft computing will also find the book invaluable. 606 $aBioinformatics 606 $aPattern recognition 606 $aArtificial intelligence 606 $aData mining 606 $aRadiology 606 $aComputational Biology/Bioinformatics$3https://scigraph.springernature.com/ontologies/product-market-codes/I23050 606 $aPattern Recognition$3https://scigraph.springernature.com/ontologies/product-market-codes/I2203X 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030 606 $aImaging / Radiology$3https://scigraph.springernature.com/ontologies/product-market-codes/H29005 615 0$aBioinformatics. 615 0$aPattern recognition. 615 0$aArtificial intelligence. 615 0$aData mining. 615 0$aRadiology. 615 14$aComputational Biology/Bioinformatics. 615 24$aPattern Recognition. 615 24$aArtificial Intelligence. 615 24$aData Mining and Knowledge Discovery. 615 24$aImaging / Radiology. 676 $a004 676 $a006.3 676 $a006.3/1 676 $a006.312 700 $aMaji$b Pradipta$4aut$4http://id.loc.gov/vocabulary/relators/aut$0973674 702 $aPaul$b Sushmita$4aut$4http://id.loc.gov/vocabulary/relators/aut 906 $aBOOK 912 $a9910298568303321 996 $aScalable Pattern Recognition Algorithms$92215723 997 $aUNINA