Machine Learning Approaches to Bioinformatics (Google eBook)

Front Cover
World Scientific, 2010 - Computers - 322 pages
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This book covers a wide range of subjects in applying machine learning approaches for bioinformatics projects. The book succeeds on two key unique features. First, it introduces the most widely used machine learning approaches in bioinformatics and discusses, with evaluations from real case studies, how they are used in individual bioinformatics projects. Second, it introduces state-of-the-art bioinformatics research methods. The theoretical parts and the practical parts are well integrated for readers to follow the existing procedures in individual research. Unlike most of the bioinformatics books on the market, the content coverage is not limited to just one subject. A broad spectrum of relevant topics in bioinformatics including systematic data mining and computational systems biology researches are brought together in this book, thereby offering an efficient and convenient platform for teaching purposes. An essential reference for both final year undergraduates and graduate students in universities, as well as a comprehensive handbook for new researchers, this book will also serve as a practical guide for software development in relevant bioinformatics projects.
  

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Contents

1 Introduction
1
2 Introduction to Unsupervised Learning
15
3 Probability Density Estimation Approaches
24
4 Dimension Reduction
38
5 Cluster Analysis
52
6 Selforganising Map
69
7 Introduction to Supervised Learning
92
8 LinearQuadratic Discriminant Analysis and Knearest Neighbour
104
12 Hidden Markov Model
177
13 Feature Selection
195
14 Feature Extraction Biological Data Coding
213
15 SequenceStructural Bioinformatics Foundation Peptide Classification
225
16 Gene Network Causal Network and Bayesian Networks
238
17 SSystems
253
18 Future Directions
269
References
279

9 Classification and Regression Trees Random Forest Algorithm
120
10 Multilayer Perceptron
133
11 Basis Function Approach and Vector Machines
154

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