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Computer and Information Science » Numerical Analysis and Scientific Computing
Bioinformatics - Trends and Methodologies
Edited by Mahmood A. Mahdavi, ISBN 978-953-307-282-1, Hard cover, 722 pages, Publisher: InTech, Chapters published November 02, 2011 under CC BY 3.0 license
DOI: 10.5772/786
Bioinformatics - Trends and Methodologies is a collection of different views on most recent topics and basic concepts in bioinformatics. This book suits young researchers who seek basic fundamentals of bioinformatic skills such as data mining, data integration, sequence analysis and gene expression analysis as well as scientists who are interested in current research in computational biology and bioinformatics including next generation sequencing, transcriptional analysis and drug design. Because of the rapid development of new technologies in molecular biology, new bioinformatic techniques emerge accordingly to keep the pace of in silico development of life science. This book focuses partly on such new techniques and their applications in biomedical science. These techniques maybe useful in identification of some diseases and cellular disorders and narrow down the number of experiments required for medical diagnostic.
- Chapter 1
Concepts, Historical Milestones and the Central Place of Bioinformatics in Modern Biology: A European Perspective - Chapter 2
Data Integration in Bioinformatics: Current Efforts and Challenges - Chapter 3
Semantic Data Integration on Biomedical Data Using Semantic Web Technologies - Chapter 4
Vector Space Information Retrieval Techniques for Bioinformatics Data Mining - Chapter 5
Massively Parallelized DNA Motif Search on FPGA - Chapter 6
A Pattern Search Method for Discovering Conserved Motifs in Bioactive Peptide Families - Chapter 7
Database Mining: Defining the Pathogenesis of Inflammatory and Immunological Diseases - Chapter 8
Data Mining Pubmed Identifies Core Signalings and miRNA Regulatory Module in Glioma - Chapter 9
Significance Score of Motifs in Biological Sequences - Chapter 10
A Systematic and Thorough Search for Domains of the Scavenger Receptor Cysteine-Rich Group-B Family in the Human Genome - Chapter 11
Assessing Multiple Sequence Alignments Using Visual Tools - Chapter 12
Optimal Sequence Alignment and Its Relationship with Phylogeny - Chapter 13
Predicting Virus Evolution - Chapter 14
A Bioinformatical Approach to Study the Endosomal Sorting Complex Required for Transport (ESCRT) Machinery in Protozoan Parasites: The Entamoeba histolytica Case - Chapter 15
Structural Bioinformatics Analysis of Acid Alpha-Glucosidase Mutants with Pharmacological Chaperones - Chapter 16
Bioinformatics Domain Structure Prediction and Homology Modeling of Human Ryanodine Receptor 2 - Chapter 17
Identifying Enzyme Knockout Strategies on Multiple Enzyme Associations - Chapter 18
Using Bacterial Artificial Chromosomes to Refine Genome Assemblies and to Build Virtual Genomes - Chapter 19
Basidiomycetes Telomeres – A Bioinformatics Approach - Chapter 20
SNPpattern: A Genetic Tool to Derive Haplotype Blocks and Measure Genomic Diversity in Populations Using SNP Genotypes - Chapter 21
Algorithms for CpG Islands Search: New Advantages and Old Problems - Chapter 22
Translational Oncogenomics and Human Cancer Interactomics: Advanced Techniques and Complex System Dynamic Approaches - Chapter 23
In-silico Approaches for RNAi Post-Transcriptional Gene Regulation: Optimizing siRNA Design and Selection - Chapter 24
MicroRNA Targeting in Heart: A Theoretical Analysis - Chapter 25
Genome-Wide Identification of Estrogen Receptor Alpha Regulated miRNAs Using Transcription Factor Binding Data - Chapter 26
Quantification of Gene Expression Based on Microarray Experiment - Chapter 27
On-Chip Living-Cell Microarrays for Network Biology - Chapter 28
Novel Machine Learning Techniques for Micro-Array Data Classification - Chapter 29
Deep Sequencing Data Analysis: Challenges and Solutions - Chapter 30
Whole Genome Annotation: In Silico Analysis - Chapter 31
Designing of Anti-Cancer Drug Targeted to Bcl-2 Associated Athanogene (BAG1) Protein
