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.
|Author||Reda Rawi [aut,cre], Matyas A. Sustik [aut], Ben Calderhead [aut]|
|Date of publication||2016-02-28 16:26:52|
|Maintainer||Reda Rawi <firstname.lastname@example.org>|
|License||GPL (>= 3)|
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