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should these parameters be set? How should
different kinds of information be weighted?
A second issue is to interpret results probabilistically. Finding a best scoring answer is
one thing, but what does the score mean,
and how confident are we that the best scoring answer is correct? A third issue is extensibility. The moment we perfect our ad hoc
genefinder, we wish we had also modeled
translational initiation consensus, alternative splicing and a polyadenylation signal.
Too often, piling more reality onto a fragile
ad hoc program makes it collapse under its
Hidden Markov models (HMMs) are a
formal foundation for making probabilistic
models of linear sequence ‘labeling’ problems1,2. They provide a conceptual toolkit
for building complex models just by draw- Sean R. Eddy is at Howard Hughes Medical
Institute & Department of Genetics,
Washington University School of Medicine,
4444 Forest Park Blvd., Box 8510, Saint Louis,
Missouri 63108, USA.
e-mail: email@example.com A = 0.25
C = 0.25
G = 0.25
T = 0.2...
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- Spring '09