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Record Nr. |
UNINA9910784816603321 |
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Titolo |
Analysis of biological data [[electronic resource] ] : a soft computing approach / / editors, Sanghamitra Bandyopadhyay, Ujjwal Maulik, Jason T.L. Wang |
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Pubbl/distr/stampa |
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Singapore ; ; Hong Kong, : World Scientific, c2007 |
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ISBN |
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1-281-91864-4 |
9786611918644 |
981-270-889-8 |
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Descrizione fisica |
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1 online resource (352 p.) |
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Collana |
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Science, engineering, and biology informatics ; ; v. 3 |
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Altri autori (Persone) |
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BandyopadhyaySanghamitra <1968-> |
MaulikUjjwal |
WangJason T. L |
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Disciplina |
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Soggetti |
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Bioinformatics |
Soft computing |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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CONTENTS; 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 |
AcknowledgementReferences; 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 |
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Emerging Areas of Soft Computing; 5.1 Artificial life |
5.2 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 José E. Gallardo, Carlos Cotta and Antonio J. Fernández; 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 |
6 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 |
4.1 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 |
5 Protein Repeat Motifs |
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Sommario/riassunto |
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Bioinformatics, 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, |
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