Biology Reference
In-Depth Information
to improving the power of pattern detection [
43
], improved performance Viterbi and
EM algorithms [
44
], and methods for speeding up HMM decoding [
45
].
The chapter includes a number of exercises of both applied and theoretical nature
and an online project
Investigating “Predicted” Genes
available from the volume's
website. We encourage the reader to attempt each exercise/project as soon as the rele-
vant material for its execution has been introduced. Most of the applied exercises use
the
CpG Educate
suite of web applications developed for this chapter and available
at
http://inspired.jsu.edu/
agarrett/cpg/
[
30
].
CpG Educate
uses the General Hidden
Markov Model library (
http://ghmm.org/
)
for the implementation of all HMM algo-
rithms. A key feature of
CpG Educate
is that it provides the option to simulate data
for most of the HMMs used in the chapter and compare the results from decoding and
training with those from the simulations. After completing the chapter exercises, we
invite the reader to undertake further experimentations with the decoding and learning
methods for various simulated and actual data.
∼
Acknowledgments
Authors Davies, Kirkwood, and Robeva gratefully acknowledge the support of the
National Science Foundation under DEU award #0737467.
9.6
SUPPLEMENTARY MATERIALS
A supplementary project and additional files and data associated with this
article can be found, in the online version, at
http://dx.doi.org/10.1016/
B978-0-12-415780-4.00019-3
and from the volume's website
References
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[3] Sorensen AL, Timoskainen S, West FD, Vekterud K, Boquest AC, Ahrlund-
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