Machine Learning in Bioinformatics / Edition 1

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Overview

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.
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Product Details

  • ISBN-13: 9780470116623
  • Publisher: Wiley, John & Sons, Incorporated
  • Publication date: 12/3/2008
  • Series: Wiley Series in Bioinformatics Series , #4
  • Edition number: 1
  • Pages: 456
  • Product dimensions: 6.00 (w) x 9.30 (h) x 1.00 (d)

Meet the Author

Yan-Qing Zhang, PhD, 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-GrC2006).

Jagath C. Rajapakse, PhD, is Professor of Computer Engineering and Director of the BioInformatics 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 PhD 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.

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Table of Contents

1 Feature Selection for Genomic and Proteomic Data Mining Sue-Yuan Kung Kung, Sue-Yuan Man-Wai Mak Mak, Man-Wai 1

2 Comparing and Visualizing Gene Selection and Classification Methods for Microarray Data Rajiv S. Menjoge Menjoge, Rajiv S. Roy E. Welsch Welsch, Roy E. 47

3 Adaptive Kernel Classifiers Via Matrix Decomposition Updating for Biological Data Analysis Hyunsoo Kim Kim, Hyunsoo Haesun Park Park, Haesun 69

4 Bootstrapping Consistency Method for Optimal Gene Selection from Microarray Gene Expression Data for Classification Problems Shaoning Pang Pang, Shaoning Ilkka Havukkala Havukkala, Ilkka Yingjie Hu Hu, Yingjie Nikola Kasabov Kasabov, Nikola 89

5 Fuzzy Gene Mining: A Fuzzy-Based Framework for Cancer Microarray Data Analysis Zhenyu Wang Wang, Zhenyu Vasile Palade Palade, Vasile 111

6 Feature Selection for Ensemble Learning and Its Application Guo-Zheng Li Li, Guo-Zheng Jack Y. Yang Yang, Jack Y. 135

7 Sequence-Based Prediction of Residue-Level Properties in Proteins Shandar Ahmad Ahmad, Shandar Yemlembam Hemjit Singh Singh, Yemlembam Hemjit Marcos J. Arauzo-Bravo Arauzo-Bravo, Marcos J. Akinori Sarai Sarai, Akinori 157

8 Consensus Approaches to Protein Structure Prediction Dongbo Bu Bu, Dongbo ShuaiCheng Li Li, ShuaiCheng Xin Gao Xin, Gao Libo Yu Yu, Libo Jinbo Xu Xu, Jinbo Ming Li Ming, Li 189

9 Kernel Methods in Protein Structure Prediction Jayavardhana Gubbi Gubbi, Jayavardhana Alistair Shilton Shilton, Alistair Marimuthu Palaniswami Palaniswami, Marimuthu 209

10 Evolutionary Granular Kernel Trees for Protein Subcellular Location Prediction Bo Jin Jin, Bo Yan-Qing Zhang Zhang, Yan-Qing 229

11 Probabilistic Models for Long-Range Features in BiosequencesLi Liao Li, Liao 241

12 Neighborhood Profile Search for Motif Refinement Chandan K. Reddy Reddy, Chandan K. Yao-Chung Weng Weng, Yao-Chung Hsiao-Dong Chiang Chiang, Hsiao-Dong 263

13 Markov/Neural Model for Eukaryotlc Promoter Recognition Jagath C. Rajapakse Rajapakse, Jagath C. Sy Loi Ho Ho, Sy Loi 283

14 Eukaryotic Promoter Detection Based on Word and Sequence Feature Selection and Combination Xudang Xie Xie, Xudang Shuanhu Wu Wu, Shuanhu Hong Yan Hong, Yan 301

15 Feature Characterization and Testing of Bidirectional Promoters in the Human Genome-Significance and Applications in Human Genome Research Mary Q. Yang Yang, Mary Q. David C. King King, David C. Laura L. Elnitski Elnitski, Laura L. 321

16 Supervised Learning Methods for MicroRNA Studies Byoung-Tak Zhang Zhang, Byoung-Tak Jin-Wu Nam Nam, Jin-Wu 339

17 Machine Learning for Computational Haplotype Analysis Phil H. Lee Lee, Phil H. Hagit Shatkay Shatkay, Hagit 367

18 Machine Learning Applications in SNP-Disease Association Study Pritam Chanda Chanda, Pritam Aidong Zhang Zhang, Aidong Murdi Ramanathan Ramanathan, Murdi 389

19 Nanopore Chemlntorrnatlcs-Based Studies of Individual Molecular Interactions Stephen Winters-Hill Winters-Hill, Stephen 413

20 An Information Fusion Framework for Biomedical Informatics Srivatsava R. Ganta Ganta, Srivatsava R. Anand Narasimhamarthy Narasimhamarthy, Anand Jyotsua Kasturi Kasturi, Jyotsua Raj Acharya Acharya, Raj 431

Index 453

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