Addressing the unmet need for visualizing conditional random fields in biological data

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… (More)
DOI: 10.1186/1471-2105-15-202

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Cite this paper

@inproceedings{Ray2014AddressingTU, title={Addressing the unmet need for visualizing conditional random fields in biological data}, author={William C. Ray and Samuel L. Wolock and Nicholas W. Callahan and Min Dong and Q. Quinn Li and Chun Liang and Thomas J. Magliery and Christopher W. Bartlett}, booktitle={BMC Bioinformatics}, year={2014} }