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E-raamat: Computational Methods With Applications In Bioinformatics Analysis

Edited by (Asia Univ, Taiwan), Edited by (Asia Univ, Taiwan & Univ Of Illinois At Chicago, Usa)
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This compendium contains 10 chapters written by world renowned researchers with expertise in semantic computing, genome sequence analysis, biomolecular interaction, time-series microarray analysis, and machine learning algorithms.The salient feature of this book is that it highlights eight types of computational techniques to tackle different biomedical applications. These techniques include unsupervised learning algorithms, principal component analysis, fuzzy integral, graph-based ensemble clustering method, semantic analysis, interolog approach, molecular simulations and enzyme kinetics.The unique volume will be a useful reference material and an inspirational read for advanced undergraduate and graduate students, computer scientists, computational biologists, bioinformatics and biomedical professionals.
Preface v
Acknowledgment vii
About the Authors ix
List of Contributors
xi
Chapter 1 Unsupervised clustering of time series gene expression data based on spectrum processing and autoregressive modeling
1(21)
Chapter 2 Gene ontology-based analysis of time series gene expression data using support vector machines
22(31)
Chapter 3 A comparative review of graph-based ensemble clustering as transformation methods for microarray data classification
53(19)
Chapter 4 Semantic analytics of biomedical data
72(26)
Chapter 5 Investigating interactions between proteins and nucleic acids by computational approaches
98(20)
Chapter 6 Bioinformatics analysis of microRNA and protein-protein interaction in plant host-pathogen interaction system
118(22)
Chapter 7 Computational modelling of the Alu-carrying RNA network in Thl7-mediated autoimmune diseases
140(13)
Chapter 8 Principal component analysis based unsupervised feature extraction applied to bioinformatics analysis
153(30)
Chapter 9 Choquet integral algorithm for T-cell epitope prediction using support vector machine
183(10)
Chapter 10 Unsupervised clustering algorithms for flow/mass cytometry data
193(14)
Index 207
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