Yanqing Zhang & Jagath C. Rajapakse 
Machine Learning in Bioinformatics 

An introduction to machine learning methods and their applications
to problems in bioinformatics

Machine learning techniques are increasingly being used to
address problems in computational biology and bioinformatics. Novel
computational techniques to analyze high throughput data in the
form of sequences, gene and protein expressions, pathways, and
images are becoming vital for understanding diseases and future
drug discovery. Machine learning techniques such as Markov models,
support vector machines, neural networks, and graphical models have
been successful in analyzing life science data because of their
capabilities in handling randomness and uncertainty of data noise
and in generalization.

From an internationally recognized panel of prominent
researchers in the field, Machine Learning in Bioinformatics
compiles recent approaches in machine learning methods and their
applications in addressing contemporary problems in bioinformatics.
Coverage includes: feature selection for genomic and proteomic data
mining; comparing variable selection methods in gene selection and
classification of microarray data; fuzzy gene mining;
sequence-based prediction of residue-level properties in proteins;
probabilistic methods for long-range features in biosequences; and
much more.

Machine Learning in Bioinformatics is an indispensable resource
for computer scientists, engineers, biologists, mathematicians,
researchers, clinicians, physicians, and medical informaticists. It
is also a valuable reference text for computer science,
engineering, and biology courses at the upper undergraduate and
graduate levels.

สารบัญ

Foreword.

Preface.

Contributors.

1 Feature Selection for Genomic and Proteomic Data Mining
(Sun-Yuan Kung and Man-Wai Mak).

2 Comparing and Visualizing Gene Selection and Classification
Methods for Microarray Data (Rajiv S. Menjoge and Roy E.
Welsch).

3 Adaptive Kernel Classifiers Via Matrix Decomposition Updating
for Biological Data Analysis (Hyunsoo Kim and Haesun
Park).

4 Bootstrapping Consistency Method for Optimal Gene Selection
from Microarray Gene Expression Data for Classification Problems
(Shaoning Pang, Ilkka Havukkala, Yingjie Hu, and Nikola
Kasabov).

5 Fuzzy Gene Mining: A Fuzzy-Based Framework for Cancer
Microarray Data Analysis (Zhenyu Wang and Vasile
Palade).

6 Feature Selection for Ensemble Learning and Its Application
(Guo-Zheng Li and Jack Y. Yang).

7 Sequence-Based Prediction of Residue-Level Properties in
Proteins (Shandar Ahmad, Yemlembam Hemjit Singh, Marcos J.
Araúzo-Bravo, and Akinori Sarai).

8 Consensus Approaches to Protein Structure Prediction
(Dongbo Bu, Shuai Cheng Li, Xin Gao, Libo Yu, Jinbo Xu, and Ming
Li).

9 Kernel Methods in Protein Structure Prediction
(Jayavardhana Gubbi, Alistair Shilton, and Marimuthu
Palaniswami).

10 Evolutionary Granular Kernel Trees for Protein Subcellular
Location Prediction (Bo Jin and Yan-Qing Zhang).

11 Probabilistic Models for Long-Range Features in Biosequences
(Li Liao).

12 Neighborhood Profile Search for Motif Refinement (Chandan
K. Reddy, Yao-Chung Weng, and Hsiao-Dong Chiang).

13 Markov/Neural Model for Eukaryotic Promoter Recognition
(Jagath C. Rajapakse and Sy Loi Ho).

14 Eukaryotic Promoter Detection Based on Word and Sequence
Feature Selection and Combination (Xudong Xie, Shuanhu Wu, and
Hong Yan).

15 Feature Characterization and Testing of Bidirectional
Promoters in the Human Genome–Significance and Applications
in Human Genome Research (Mary Q. Yang, David C. King, and Laura
L. Elnitski).

16 Supervised Learning Methods for Micro RNA Studies
(Byoung-Tak Zhang and Jin-Wu Nam).

17 Machine Learning for Computational Haplotype Analysis
(Phil H. Lee and Hagit Shatkay).

18 Machine Learning Applications in SNP-Disease
Association Study (Pritam Chanda, Aidong Zhang, and Murali
Ramanathan).

19 Nanopore Cheminformatics-Based Studies of Individual
Molecular Interactions (Stephen Winters-Hilt).

20 An Information Fusion Framework for Biomedical Informatics
(Srivatsava R. Ganta, Anand Narasimhamurthy, Jyotsna Kasturi,
and Raj Acharya).

Index.

เกี่ยวกับผู้แต่ง

Yan-Qing Zhang, Ph D, is an Associate Professor of Computer Science at the Georgia State University, Atlanta. His research interests include hybrid intelligent systems, neural networks, fuzzy logic, evolutionary computation, Yin-Yang computation, granular computing, kernel machines, bioinformatics, medical informatics, computational Web Intelligence, data mining, and knowledge discovery. He has coauthored two books, and edited one book and two IEEE proceedings. He is program co-chair of the IEEE 7th International Conference on Bioinformatics & Bioengineering (IEEE BIBE 2007) and 2006 IEEE International Conference on Granular Computing (IEEE-Gr C2006).

Jagath C. Rajapakse, Ph D, is Professor of Computer Engineering and Director of the Bio Informatics Research Centre, Nanyang Technological University. He is also Visiting Professor in the Department of Biological Engineering, Massachusetts Institute of Technology. He completed his MS and Ph D degrees in electrical and computer engineering at University at Buffalo, State University of New York. Professor Rajapakse has published over 210 peer-reviewed research articles in the areas of neuroinformatics and bioinformatics. He serves as Associate Editor for IEEE Transactions on Medical Imaging and IEEE/ACM Transactions on Computational Biology and Bioinformatics.