Computational Biology

PUBLISHED


Viewing: 1 - 10 of 67
Published:
September 19, 2014
Author(s):
Eija Korpelainen, Jarno Tuimala, Panu Somervuo, Mikael Huss, Garry Wong
The State of the Art in Transcriptome AnalysisRNA sequencing (RNA-seq) data offers unprecedented information about the transcriptome, but harnessing this information with bioinformatics tools is typically a bottleneck. RNA-seq Data Analysis: A Practical Approach enables researchers to examine 
Published:
August 21, 2014
Editor(s):
Ruhong Zhou
Although molecular modeling has been around for a while, the groundbreaking advancement of massively parallel supercomputers and novel algorithms for parallelization is shaping this field into an exciting new area. Developments in molecular modeling from experimental and computational techniques 
Published:
July 29, 2014
Author(s):
Forbes J. Burkowski
A Step-by-Step Guide to Describing Biomolecular Structure Computational and Visualization Techniques for Structural Bioinformatics Using Chimera shows how to perform computations with Python scripts in the Chimera environment. It focuses on the three core areas needed to study structural 
Published:
July 25, 2014
Editor(s):
Bernard Querleux
The accessibility of the skin in vivo has resulted in the development of non-invasive methods in the past 40 years that offer accurate measurements of skin properties and structures from microscopic to macroscopic levels. However, the mechanisms involved in these properties are still only partly 
Published:
June 20, 2014
Author(s):
Marco Scutari, Jean-Baptiste Denis
Understand the Foundations of Bayesian Networks—Core Properties and Definitions Explained Bayesian Networks: With Examples in R introduces Bayesian networks using a hands-on approach. Simple yet meaningful examples in R illustrate each step of the modeling process. The examples start from the 
Published:
May 27, 2014
Editor(s):
Ming-Hui Chen, Lynn Kuo, Paul O. Lewis
Offering a rich diversity of models, Bayesian phylogenetics allows evolutionary biologists, systematists, ecologists, and epidemiologists to obtain answers to very detailed phylogenetic questions. Suitable for graduate-level researchers in statistics and biology, Bayesian Phylogenetics: Methods, 
Published:
March 18, 2014
Author(s):
Allegra Via, Kristian Rother, Anna Tramontano
Take Control of Your Data and Use Python with Confidence Requiring no prior programming experience, Managing Your Biological Data with Python empowers biologists and other life scientists to work with biological data on their own using the Python language. The book teaches them not only how to 
Published:
March 06, 2014
Editor(s):
Luis Rueda
Microarray Image and Data Analysis: Theory and Practice is a compilation of the latest and greatest microarray image and data analysis methods from the multidisciplinary international research community. Delivering a detailed discussion of the biological aspects and applications of microarrays, the 
Published:
December 18, 2013
Author(s):
Guanyu Wang, PhD
A complex disease involves many etiological and risk factors operating at multiple levels—molecular, cellular, organismal, and environmental. The incidence of such diseases as cancer, obesity, and diabetes are increasing in occurrence, urging us to think fundamentally and use a broader perspective 
Published:
December 17, 2013
Author(s):
Ken A. Aho
Full of biological applications, exercises, and interactive graphical examples, Foundational and Applied Statistics for Biologists Using R presents comprehensive coverage of both modern analytical methods and statistical foundations. The author harnesses the inherent properties of the R environment 

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Data Mining "Nobel Prize" Awarded to Series Editor Vipin Kumar

The ACM SIGKDD 2012 Innovation Award, the highest award for technical excellence in the field of Knowledge Discovery and Data Mining (KDD), has been awarded to Prof. Vipin Kumar for his technical contributions to foundational research in data mining and its applications to mining scientific and climate data. Prof. Kumar is the editor of the Chapman & Hall/CRC Data Mining and Knowledge Discovery Series. He has pioneered research in the areas of high performance and parallel data mining. His research group has also been at the forefront in the development of data-driven discovery methods for analyzing global climate and ecosystem data.

 

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