Computational Biology and Bioinformatics

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alignment
biomedical data mining
Bisulfite Sequencing
Category=PS
Category=UY
ChIP Seq Data
ChIP Seq Experiment
ChIP Seq Signal
data normalisation
DNA Methylation
DNA Methylation Level
EGFR Mutant
EGFR Mutation Positive NSCLCs
EGFR TK
EGFR TK Domain
enhancer prediction algorithms
Enhancer Target
epigenetics
eq_bestseller
eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
eq_science
expression
gene
Gene Body Methylation
genome
genome analysis
GLR Test
Granger Causality
Graphical Processing Unit
histone
Histone Modification
Intragenic DNA Methylation
Irreversible TKIs
modifications
molecular phylogenetics
multiple
Pairwise Sequence Alignment
PCA Model
protein annotation techniques
Protein Coding Genes
protein structure predictions
protein-coding
reference
Reference Genomes
RNA sequencing methods
sequence
systems biology
Time Series Gene Expression Data
transcription factor binding sites
WT EGFR

Product details

  • ISBN 9781498724975
  • Weight: 975g
  • Dimensions: 152 x 229mm
  • Publication Date: 09 May 2016
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Hardback
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The advances in biotechnology such as the next generation sequencing technologies are occurring at breathtaking speed. Advances and breakthroughs give competitive advantages to those who are prepared. However, the driving force behind the positive competition is not only limited to the technological advancement, but also to the companion data analytical skills and computational methods which are collectively called computational biology and bioinformatics. Without them, the biotechnology-output data by itself is raw and perhaps meaningless. To raise such awareness, we have collected the state-of-the-art research works in computational biology and bioinformatics with a thematic focus on gene regulation in this book.

This book 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, we believe that this book will serve as a useful reference for both bioinformaticians and computational biologists in the post-genomic era.

Ka-Chun Wong