Biological data mining and its applications in healthcare /

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Biologists are stepping up their efforts in understanding the biological processes that underlie disease pathways in the clinical contexts. This has resulted in a flood of biological and clinical data from genomic and protein sequences, DNA microarrays, protein interactions, biomedical images, to di...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Li, Xiao-Li, 1969- (Editor ), Ng, See-Kiong (Editor ), Wang, Jason T. L. (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: New Jersey : World Scientific, 2013.
Colección:Science, engineering, and biology informatics ; v. 8.
Temas:
Medical informatics.
Bioinformatics.
Data mining.
Natural language processing (Computer science)
Computational Biology > methods
Data Mining > methods
Genomics > methods
Natural Language Processing
Imaging, Three-Dimensional > methods
Computational Biology
Data Mining
Médecine > Informatique.
Bio-informatique.
Exploration de données (Informatique)
Traitement automatique des langues naturelles.
HEALTH & FITNESS > Holism.
HEALTH & FITNESS > Reference.
MEDICAL > Alternative Medicine.
MEDICAL > Atlases.
MEDICAL > Essays.
MEDICAL > Family & General Practice.
MEDICAL > Holistic Medicine.
MEDICAL > Osteopathy.
Bioinformatics
Data mining
Medical informatics
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 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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