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.

Getting started

Package details

AuthorReda Rawi [aut,cre], Matyas A. Sustik [aut], Ben Calderhead [aut]
MaintainerReda Rawi <[email protected]>
LicenseGPL (>= 3)
Package repositoryView on CRAN
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COUSCOus documentation built on May 2, 2019, 9:27 a.m.