LEADER 05551nam 2200685Ia 450 001 9910784816603321 005 20230721031043.0 010 $a1-281-91864-4 010 $a9786611918644 010 $a981-270-889-8 035 $a(CKB)1000000000410199 035 $a(EBL)1679434 035 $a(OCoLC)879074113 035 $a(SSID)ssj0000102921 035 $a(PQKBManifestationID)11127529 035 $a(PQKBTitleCode)TC0000102921 035 $a(PQKBWorkID)10061002 035 $a(PQKB)11127995 035 $a(MiAaPQ)EBC1679434 035 $a(WSP)00006493 035 $a(Au-PeEL)EBL1679434 035 $a(CaPaEBR)ebr10255364 035 $a(CaONFJC)MIL191864 035 $a(EXLCZ)991000000000410199 100 $a20070728d2007 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aAnalysis of biological data$b[electronic resource] $ea soft computing approach /$feditors, Sanghamitra Bandyopadhyay, Ujjwal Maulik, Jason T.L. Wang 210 $aSingapore ;$aHong Kong $cWorld Scientific$dc2007 215 $a1 online resource (352 p.) 225 1 $aScience, engineering, and biology informatics ;$vv. 3 300 $aDescription based upon print version of record. 311 $a981-270-780-8 320 $aIncludes bibliographical references and index. 327 $aCONTENTS; Preface; Part I OVERVIEW; Chapter 1 Bioinformatics: Mining the Massive Data from High Throughput Genomics Experiments Haixu Tang and Sun Kim; 1 Introduction; 2 Recent Development of Classical Topics; 2.1 Sequence alignment; 2.2 Genome sequencing and fragment assembly; 2.3 Gene annotation; 2.4 RNA folding; 2.5 Motif finding; 2.6 Protein structure prediction; 3 Emerging Topics from New Genome Technologies; 3.1 Comparative genomics: beyond genome comparison; 3.2 Pathway reconstruction; 3.3 Microarray analysis; 3.4 Proteomics; 3.5 Protein-protein interaction; 4 Conclusion 327 $aAcknowledgementReferences; Chapter 2 An Introduction to Soft Computing Amit Konar and Swagatam Das; 1 Classical AI and its Pitfalls; 2 What is Soft Computing?; 3 Fundamental Components of Soft Computing; 3.1 Fuzzy sets and fuzzy logic; 3.2 Neural networks; 3.3 Genetic algorithms; 3.4 Belief networks; 4 Synergism in Soft Computing; 4.1 Neuro-fuzzy synergism; 4.2 Neuro-GA synergism; 4.3 Fuzzy-GA synergism; 4.4 Neuro-belief network synergism; 4.5 GA-belief network synergism; 4.6 Neuro-fuzzy-GA synergism; 5 Some Emerging Areas of Soft Computing; 5.1 Artificial life 327 $a5.2 Particle swarm optimization (PSO)5.3 Artificial immune system; 5.4 Rough sets and granular computing; 5.5 Chaos theory; 5.6 Ant colony systems (ACS); 6 Summary; References; Part II BIOLOGICAL SEQUENCE AND STRUCTURE ANALYSIS; Chapter 3 Reconstructing Phylogenies with Memetic Algorithms and Branch-and-Bound Jose? E. Gallardo, Carlos Cotta and Antonio J. Ferna?ndez; 1 Introduction; 2 A Crash Introduction to Phylogenetic Inference; 3 Evolutionary Algorithms for the Phylogeny Problem; 4 A BnB Algorithm for Phylogenetic Inference; 5 A Memetic Algorithm for Phylogenetic Inference 327 $a6 A Hybrid Algorithm7 Experimental Results; 7.1 Experimental setting; 7.2 Sensitivity analysis on the hybrid algorithm; 7.3 Analysis of results; 8 Conclusions; Acknowledgment; References; Chapter 4 Classification ofRNASequences with Support Vector Machines Jason T. L. Wang and Xiaoming Wu; 1 Introduction; 2 Count Kernels and Marginalized Count Kernels; 2.1 RNA sequences with known secondary structures; 2.2 RNA sequences with unknown secondary structures; 3 Kernel Based on Labeled Dual Graphs; 3.1 Labeled dual graphs; 3.2 Marginalized kernel for labeled dual graphs; 4 A New Kernel 327 $a4.1 Extracting features for global structural information4.2 Extracting features for local structural information; 5 Experiments and Results; 5.1 Data and parameters; 5.2 Results; 6 Conclusion; Acknowledgment; References; Chapter 5 Beyond String Algorithms: Protein Sequence Analysis using Wavelet Transforms Arun Krishnan and Kuo-Bin Li; 1 Introduction; 1.1 String algorithms; 1.2 Sequence analysis; 1.3 Wavelet transform; 2 Motif Searching; 2.1 Introduction; 2.2 Methods; 2.3 Results; 2.4 Allergenicity prediction; 3 Transmembrane Helix Region (HTM) Prediction; 4 Hydrophobic Cores 327 $a5 Protein Repeat Motifs 330 $aBioinformatics, a field devoted to the interpretation and analysis of biological data using computational techniques, has evolved tremendously in recent years due to the explosive growth of biological information generated by the scientific community. Soft computing is a consortium of methodologies that work synergistically and provides, in one form or another, flexible information processing capabilities for handling real-life ambiguous situations. Several research articles dealing with the application of soft computing tools to bioinformatics have been published in the recent past; however, 410 0$aScience, engineering, and biology informatics ;$vv. 3. 606 $aBioinformatics 606 $aSoft computing 615 0$aBioinformatics. 615 0$aSoft computing. 676 $a570.28563 701 $aBandyopadhyay$b Sanghamitra$f1968-$0471674 701 $aMaulik$b Ujjwal$0937858 701 $aWang$b Jason T. L$0931445 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910784816603321 996 $aAnalysis of biological data$93775483 997 $aUNINA