fmpl is a Fortran code for structure learning of discrete graphical models. fmpl takes as input a n x p data matrix and models the underlying p-dimensional probability distribution with a undirected graphical model of (pairwise interacting) discrete random variables. The output of the code is a set of p * (p - 1) / 2 scores measuring the statistical interaction between each pair of variables. More specifically, the scores are computed as corrected Frobenius norm scores (see Ekeberg et al.) from the coupling parameters obtained by performing a (multinomial) logistic regression of each variable on the remaining variables (see Ravikumar et al.). We used fmpl to predict chromatin-related protein-protein interactions (details in our paper "Epigenomic co-localization and co-evolution reveal a key role for 5hmC as a communication hub in the chromatin network of ESCs") and contacts located at interfaces of eukaryotic protein heterodimers ("Conservation of coevolving protein interfaces bridges prokaryote--eukaryote homologies in the twilight zone").
Copyright (c) 2015-2017, Simone Marsili All rights reserved.
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If this software has been useful for your work, please cite using the following bibtex entry:
@misc{fmpl,
author = {Simone Marsili},
title = {fmpl 1.0},
month = jun,
year = 2017,
doi = {10.5281/zenodo.809400},
url = {http://dx.doi.org/10.5281/zenodo.809400}
}