Machine learning approaches to bioinformatics / Zheng Rong Yang.

Format:

Book

Author/Creator:

Yang, Zheng Rong.

Series:

Science, engineering, and biology informatics ; v. 4.

Science, engineering, and biology informatics ; v. 4

Language:

English

Subjects (All):

Bioinformatics.

Machine learning.

Bioinformatics--Case studies.

Machine learning--Case studies.

Physical Description:

1 online resource (336 p.)

Edition:

1st ed.

Place of Publication:

Singapore ; Hackensack, N.J. : World Scientific, c2010.

Language Note:

English

Summary:

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. Furthermore, the book includes R codes and example data sets to help readers develop their own bioinformatics research skills. The theoretical parts and the practical par

Contents:

Preface; Contents; 1 Introduction; 2 Introduction to Unsupervised Learning; 3 Probability Density Estimation Approaches; 4 Dimension Reduction; 5 Cluster Analysis; 6 Self-organising Map; 7 Introduction to Supervised Learning; 8 Linear/Quadratic Discriminant Analysis and K-nearest Neighbour; 9 Classification and Regression Trees, Random Forest Algorithm; 10 Multi-layer Perceptron; 11 Basis Function Approach and Vector Machines; 12 Hidden Markov Model; 13 Feature Selection; 14 Feature Extraction (Biological Data Coding); 15 Sequence/Structural Bioinformatics Foundation - Peptide Classification

16 Gene Network - Causal Network and Bayesian Networks17 S-Systems; 18 Future Directions; References; Index

Notes:

Description based upon print version of record.

Includes bibliographical references and index.

ISBN:

9786612761485

9781282761483

128276148X

9789814287319

9814287318

OCLC:

670430124