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Books > Science & Mathematics > Physics > Applied physics & special topics > Biophysics
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Computational Systems Biology Approaches in Cancer Research (Paperback)
Loot Price: R1,635
Discovery Miles 16 350
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Computational Systems Biology Approaches in Cancer Research (Paperback)
Series: Chapman & Hall/CRC Computational Biology Series
Expected to ship within 10 - 15 working days
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Praise for Computational Systems BiologyApproaches in Cancer
Research: "Complex concepts are written clearly and with
informative illustrations and useful links. The book is enjoyable
to read yet provides sufficient depth to serve as a valuable
resource for both students and faculty." - Trey Ideker, Professor
of Medicine, UC Xan Diego, School of Medicine "This volume is
attractive because it addresses important and timely topics for
research and teaching on computational methods in cancer research.
It covers a broad variety of approaches, exposes recent innovations
in computational methods, and provides acces to source code and to
dedicated interactive web sites." - Yves Moreau, Department of
Electrical Engineering, SysBioSys Centre for Computational Systems
Biology, University of Leuven With the availability of massive
amounts of data in biology, the need for advanced computational
tools and techniques is becoming increasingly important and key in
understanding biology in disease and healthy states. This book
focuses on computational systems biology approaches, with a
particular lens on tackling one of the most challenging diseases -
cancer. The book provides an important reference and teaching
material in the field of computational biology in general and
cancer systems biology in particular. The book presents a list of
modern approaches in systems biology with application to cancer
research and beyond. It is structured in a didactic form such that
the idea of each approach can easily be grasped from the short text
and self-explanatory figures. The coverage of topics is diverse:
from pathway resources, through methods for data analysis and
single data analysis to drug response predictors, classifiers and
image analysis using machine learning and artificial intelligence
approaches. Features Up to date using a wide range of approaches
Applicationexample in each chapter Online resources with useful
applications'
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