Computational Biology and Bioinformatics

Gene Regulation
Herausgeber: Wong, Ka-Chun

• Gebundenes Buch

Produktbeschreibung

This book presents the latest research in computational biology and bioinformatics, with a thematic focus on gene regulation. It is designed to be self-contained and comprehensive, targeting senior undergraduates and junior graduate students in the related disciplines such as bioinformatics, computational biology, biostatistics, genome science, computer science, applied data mining, applied machine learning, life science, biomedical science, and genetics. In addition, this book serves as a useful reference for both bioinformaticians and computational biologists.

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Produktdetails

• Verlag: Taylor & Francis Ltd (Sales)
• Seitenzahl: 438
• Erscheinungstermin: 9. Mai 2016
• Englisch
• Abmessung: 234mm x 157mm x 28mm
• Gewicht: 975g
• ISBN-13: 9781498724975
• ISBN-10: 1498724973
• Artikelnr.: 43922894

Autorenporträt

Ka-Chun Wong

Inhaltsangabe

A Survey on Computational Methods for Enhancer and Enhancer Target
Predictions. Cormotif: an R Package for Jointly Detecting Differential Gene
Expression in Multiple Studies. Granger Causality for Time Series Gene
Expression Data. RNA Sequencing and Gene Expression Regulation. Modern
Technologies and Approaches for Decoding Non-coding RNA-mediated Biological
Networks in Systems Biology and Their Applications. Annotation of
Hypothetical Proteins, a Functional Genomics Approach. Protein-Protein
Functional Linkage Predictions: Bringing Regulation to Context. Epigenomic
Analysis of Chromatin Organization and DNA Methylation. Gene Body
Methylation and Transcriptional Regulation: Statistical Modelling and More.
Computational Characterization of Non-small-cell Lung Cancerwith EGFR Gene
Mutations and Its Application to Drug Resistance Prediction. Quality
Assurance in Genome-Scale Bioinformatics Analyses. Recent Computational
Trends in Biological Sequence Alignment. 13. State Estimation and Process
Monitoring of Nonlinear Biological Phenomena Modeled by S-systems.
Next-Generation Sequencing and Metagenomics. METABOLIC ENGINEERING- Its
Dimensions and Applications. Methods to Identify Evolutionary Conserved
Regulatory Elements Using Molecular Phylogenetics in Microbes. Improved
Protein Model Ranking through Topological Assessment.