Pattern Recognition in Bioinformatics

1. Front Matter

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2. Pattern Recognition in Bioinformatics: An Introduction

   1. Pattern Recognition in Bioinformatics: An Introduction

      • J. C. Rajapakse, L. Wong, R. Acharya

      Pages 1-3

3. Part 1: Signal and Motif Detection; Gene Selection

   1. Machine Learning Prediction of Amino Acid Patterns in Protein N-myristoylation

      • Ryo Okada, Manabu Sugii, Hiroshi Matsuno, Satoru Miyano

      Pages 4-14

   2. A Profile HMM for Recognition of Hormone Response Elements

      • Maria Stepanova, Feng Lin, Valerie C. -L. Lin

      Pages 15-22

   3. Graphical Approach to Weak Motif Recognition in Noisy Data Sets

      • Loi Sy Ho, Jagath C. Rajapakse

      Pages 23-31

   4. Comparative Gene Prediction Based on Gene Structure Conservation

      • Shu Ju Hsieh, Chun Yuan Lin, Ning Han Liu, Chuan Yi Tang

      Pages 32-41

   5. Computational Identification of Short Initial Exons

      • Sayanthan Logeswaran, Eliathamby Ambikairajah, Julien Epps

      Pages 42-48

   6. Pareto-Gamma Statistic Reveals Global Rescaling in Transcriptomes of Low and High Aggressive Breast Cancer Phenotypes

      • Alvin L. -S. Chua, Anna V. Ivshina, Vladimir A. Kuznetsov

      Pages 49-59

   7. Investigating the Class-Specific Relevance of Predictor Sets Obtained from DDP-Based Feature Selection Technique

      • Chia Huey Ooi, Madhu Chetty, Shyh Wei Teng

      Pages 60-70

   8. A New Maximum-Relevance Criterion for Significant Gene Selection

      • Young Bun Kim, Jean Gao, Pawel Michalak

      Pages 71-80

4. Part 2: Models of DNA, RNA, and Protein Structures

   1. Spectral Graph Partitioning Analysis of In Vitro Synthesized RNA Structural Folding

      • Stanley NG Kwang Loong, Santosh K. Mishra

      Pages 81-92

   2. Predicting Secondary Structure of All-Helical Proteins Using Hidden Markov Support Vector Machines

      • Blaise Gassend, Charles W. O’Donnell, William Thies, Andrew Lee, Marten van Dijk, Srinivas Devadas

      Pages 93-104

   3. Prediction of Protein Subcellular Localizations Using Moment Descriptors and Support Vector Machine

      • Jianyu Shi, Shaowu Zhang, Yan Liang, Quan Pan

      Pages 105-114

   4. Using Permutation Patterns for Content-Based Phylogeny

      • Md Enamul Karim, Laxmi Parida, Arun Lakhotia

      Pages 115-125

5. Part 3: Biological Databases and Imaging

   1. The Immune Epitope Database and Analysis Resource

      • A. Sette, H. H. Bui, J. Sidney, P. Bourne, S. Buus, W. Fleri et al.

      Pages 126-132

   2. Intelligent Extraction Versus Advanced Query: Recognize Transcription Factors from Databases

      • Zhuo Zhang, Merlin Veronika, See-Kiong Ng, Vladimir B Bajic

      Pages 133-139

   3. Incremental Maintenance of Biological Databases Using Association Rule Mining

      • Kai-Tak Lam, Judice L. Y. Koh, Bharadwaj Veeravalli, Vladimir Brusic

      Pages 140-150

   4. Blind Separation of Multichannel Biomedical Image Patterns by Non-negative Least-Correlated Component Analysis

      • Fa-Yu Wang, Yue Wang, Tsung-Han Chan, Chong-Yung Chi

      Pages 151-162

   5. Image and Fractal Information Processing for Large-Scale Chemoinformatics, Genomics Analyses and Pattern Discovery

      • Ilkka Havukkala, Lubica Benuskova, Shaoning Pang, Vishal Jain, Rene Kroon, Nikola Kasabov

      Pages 163-173

   6. Hybridization of Independent Component Analysis, Rough Sets, and Multi-Objective Evolutionary Algorithms for Classificatory Decomposition of Cortical Evoked Potentials

      • Tomasz G. Smolinski, Grzegorz M. Boratyn, Mariofanna Milanova, Roger Buchanan, Astrid A. Prinz

      Pages 174-183

The field of bioinformatics has two main objectives: the creation and maintenance of biological databases, and the discovery of knowledge from life sciences data in order to unravel the mysteries of biological function, leading to new drugs and therapies for human disease. Life sciences data come in the form of biological sequences, structures, pathways, or literature. One major aspect of discovering biological knowledge is to search, predict, or model specific patterns of a given dataset, which have some relevance to an important biological phenomenon or another dataset. To date, many pattern recognition algorithms have been applied or catered to address a wide range of bioinformatics problems. The 2006 Workshop of Bioinformatics in Pattern Recognition (PRIB 2006) marks the beginning of a series of workshops that is aimed at gathering researchers applying pattern recognition algorithms in an attempt to resolve problems in computational biology and bioinformatics. This volume presentsthe proceedings of Workshop PRIB 2006 held in Hong Kong, China, on August 20, 2006. It includes 19 technical contributions that were selected by the Program Committee from 43 submissions. We give a brief introduction to pattern recognition in bioinformatics in the first paper. The rest of the volume consists of three parts. Part 1: signal and motif detection, and gene selection. Part 2: models of DNA, RNA, and protein structures. Part 3: biological databases and imaging.

• Microarray
• algorithms
• association rule mining
• bioinformatics
• biomedical data analysis
• biomedical informatics
• classifcation
• data management
• data mining
• databases
• feature selection
• gene prediction
• genome
• pattern
• pattern recognition

• Singapore-MIT Alliance, Singapore

  Jagath C. Rajapakse

• School of Computing, National University of Singapore, Singapore

  Limsoon Wong

• Computer Science and Engineering, The Penn State University, USA

  Raj Acharya

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