Biological data mining and its applications in healthcare

Responsibility [edited by] Xiaoli Li (A*STAR, Singapore & Nanyang Technological University, Singapore), See-Kiong Ng (A*STAR, Singapore), & Jason T.L. Wang (New Jersey Institute of Technology, USA). Publication New Jersey : World Scientific, 2013. Physical description 1 online resource Series Science, engineering, and biology informatics ; v. 8.

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Description

Creators/Contributors

Contributor Li, Xiao-Li, 1969- editor. Ng, See-Kiong, editor. Wang, Jason T. L., editor.

Contents/Summary

• Contents
• Preface
• Part I: Sequence Analysis
• Mining the Sequence Databases for Homology Detection: Application to Recognition of Functions of Trypanosoma brucei brucei Proteins and Drug Targets
• 1. Introduction
• 2. Remote homology driven approaches for protein function annotation
• 2.1. Sequence-based approaches for remote homology detection
• 2.1.1. Iterated searches using PSI-BLAST
• 2.1.2. Multi-profiles approach to improve sensitivity
• 2.1.3. Cascade PSI-BLAST
• 2.1.4. Hidden Markov Models
• 2.1.5. Profile-profile matching algorithms

• 2.2. Assessment of significant sequence alignments3. Trypanosoma brucei: A case study
• 3.1. Overview on structural and functional domain assignments in T. brucei proteome
• 3.2. Fold assignments
• 3.3. Metabolic proteins in Trypanosoma brucei
• 3.3.1. Domain composition of metabolic proteins
• 3.3.2. Predicting drug targets based on remote homology approaches
• 4. Conclusions
• Acknowledgments
• References
• Identification of Genes and their Regulatory Regions Based on Multiple Physical and Structural Properties of a DNA Sequence
• 1. Introduction

• 2. Gene prediction methods2.1. Background
• 2.2. Exon prediction based on the AR model and multifeature spectral analysis
• 3. Regulatory region (promoter) prediction methods
• 3.1. Background
• 3.2. Cascade AdaBoost algorithm
• 3.3. Hierarchical promoter prediction system based on signal, context and structural properties
• 3.4. Prediction of eukaryotic core promoters based on Isomap and support vector machine
• 3.5. Computational identification of disease-related genes and regulatory regions
• 4. Summary
• Acknowledgement
• References

• Mining Genomic Sequence Data for Related Sequences Using Pairwise Statistical Significance1. Introduction
• 1.1. Biological sequence
• 1.2. Homology and similarity
• 1.3. Sequence alignment
• 2. Statistical significance
• 2.1. Why statistical significance?
• 2.2. P-value in statistical significance
• 2.3. Modeling statistical for local sequence alignment
• 2.3.1. Coin-Toss model
• 2.3.2. Assessing the statistical significance using alignment scores
• 2.4. Gumbel extreme value distribution
• 3. Pairwise statistical significance

• 3.1. The definition of pairwise statistical significance3.2. Parameters fitting of pairwise statistical significance
• 3.3. Evaluation of pairwise statistical significance
• 4. HPC solutions for accelerating pairwise statistical significance estimation
• 4.1. Parallel paradigms of HPC techniques
• 4.2. Implementations
• 4.3. Summary
• Acknowledgement
• References
• Part II: Biological Network Mining
• Indexing for Similarity Queries on Biological Networks
• 1. Introduction
• 2. Preliminaries
• 2.1. Definitions
• 2.2. Problem Formulation

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Bibliographic information

Publication date 2013 Series Science, engineering, and biology informatics ; volume 8 ISBN 9789814551014 (electronic bk.) 9814551015 (electronic bk.) 9789814551007 9814551007

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Start at call number: R859.7.D35 B56 2013eb