Computational Methods With Applications In Bioinformatics Analysis
eBook - ePub

Computational Methods With Applications In Bioinformatics Analysis

0

  1. 232 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

Computational Methods With Applications In Bioinformatics Analysis

0

About this book

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.

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Yes, you can access Computational Methods With Applications In Bioinformatics Analysis by Jeffrey J P Tsai, Ka-Lok Ng in PDF and/or ePUB format, as well as other popular books in Computer Science & Bioinformatics. We have over one million books available in our catalogue for you to explore.

Information

Publisher
WSPC
Year
2017
eBook ISBN
9789813207998
Topic
Computer Science
Subtopic
Bioinformatics
Index
Computer Science

Table of contents

  1. Cover
  2. Halftitle
  3. Series Editors
  4. Title
  5. Copyright
  6. Preface
  7. Acknowledgement
  8. About the Authors
  9. List of Contributors
  10. Contents
  11. Chapter 1. Unsupervised clustering of time series gene expression data based on spectrum processing and autoregressive modeling
  12. Chapter 2. Gene ontology-based analysis of time series gene expression data using support vector machines
  13. Chapter 3. A comparative review of graph-based ensemble clustering as transformation methods for microarray data classification
  14. Chapter 4. Semantic analytics of biomedical data
  15. Chapter 5. Investigating interactions between proteins and nucleic acids by computational approaches
  16. Chapter 6. Bioinformatics analysis of microRNA and protein-protein interaction in plant host-pathogen interaction system
  17. Chapter 7. Computational modelling of the Alu-carrying RNA network in Th17-mediated autoimmune diseases
  18. Chapter 8. Principal component analysis based unsupervised feature extraction applied to bioinformatics analysis
  19. Chapter 9. Choquet integral algorithm for T-cell epitope prediction using support vector machine
  20. Chapter 10. Unsupervised clustering algorithms for flow/mass cytometry data
  21. Index