Digital Signal Processing Reference
In-Depth Information
13
Genetic Regulatory Networks: A Nonlinear
Signal Proc
essing Perspective
Ilya Shmulevich and Edward R. Dougherty
CONTENTS
13.1
Introduction
.........................................................
507
13.2
Genetic Regulatory Networks
.......................................
508
13.3
Boolean Networks
...................................................
510
13.4
Concluding Remarks
................................................
518
References
..................................................................
519
13.1
Introduction
The term
functional genomics
refers to the study of how genes affect biolog-
ical mechanisms and phenotype, in particular by applying large-scale and
high-throughput experimental methods. The application of computational
methods to these and other related problems is referred to as
computational
genomics
. This discipline has been highly influenced by data mining, partly
due to the availability of large data sets and databases. Although data mining,
as a discipline, is quite broad and lies at the intersection of statistics, machine
learning, pattern recognition, and artificial intelligence,
1
there are a number
of challenging and important problems in computational genomics that can
benefit from the application of engineering principles and methodologies, the
latter being characterized by systems-level modeling and simulation.
Modern nonlinear signal processing, although encompassing many of the
same subject areas, has had a different history and background, being rooted
mostly in traditional signal processing. As such, the applications around
which the field has developed have been of a nature substantially different
from those in data mining. Whereas data-mining problems are often centered
around visualization and exploratory analysis of large, high-dimensional
data sets, finding patterns in data and discovering good feature sets for
Search WWH ::
Custom Search