COUSCOus: A Residue-Residue Contact Detecting Method
Contact prediction using shrinked covariance (COUSCOus). COUSCOus is a residue-residue contact detecting method approaching the contact inference using the glassofast implementation of Matyas and Sustik (2012, The University of Texas at Austin UTCS Technical Report 2012:1-3. TR-12-29.) that solves the L_1 regularised Gaussian maximum likelihood estimation of the inverse of a covariance matrix. Prior to the inverse covariance matrix estimation we utilise a covariance matrix shrinkage approach, the empirical Bayes covariance estimator, which has been shown by Haff (1980) <DOI:10.1214/aos/1176345010> to be the best estimator in a Bayesian framework, especially dominating estimators of the form aS, such as the smoothed covariance estimator applied in a related contact inference technique PSICOV.
- Reda Rawi [aut,cre], Matyas A. Sustik [aut], Ben Calderhead [aut]
- Date of publication
- 2016-02-28 16:26:52
- Reda Rawi <email@example.com>
- GPL (>= 3)
- Contact prediction using shrinked covariance.
- Internal COUSCOus functions
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