Title |
Addressing the unmet need for visualizing conditional random fields in biological data
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Published in |
BMC Bioinformatics, July 2014
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DOI | 10.1186/1471-2105-15-202 |
Pubmed ID | |
Authors |
William C Ray, Samuel L Wolock, Nicholas W Callahan, Min Dong, Q Quinn Li, Chun Liang, Thomas J Magliery, Christopher W Bartlett |
Abstract |
The biological world is replete with phenomena that appear to be ideally modeled and analyzed by one archetypal statistical framework - the Graphical Probabilistic Model (GPM). The structure of GPMs is a uniquely good match for biological problems that range from aligning sequences to modeling the genome-to-phenome relationship. The fundamental questions that GPMs address involve making decisions based on a complex web of interacting factors. Unfortunately, while GPMs ideally fit many questions in biology, they are not an easy solution to apply. Building a GPM is not a simple task for an end user. Moreover, applying GPMs is also impeded by the insidious fact that the "complex web of interacting factors" inherent to a problem might be easy to define and also intractable to compute upon. |
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