This is the code which accompanies the paper "Fast and accurate multivariate Gaussian modeling of protein families: Predicting residue contacts and protein-interaction partners" by Carlo Baldassi, Marco Zamparo, Christoph Feinauer, Andrea Procaccini, Riccardo Zecchina, Martin Weigt and Andrea Pagnani, (2014) PLoS ONE 9(3): e92721. doi:10.1371/journal.pone.0092721
You can download the Julia code from the attached file "GaussDCA-julia.tgz"; however, the recommended way to obtain the code is by using the command
You can download the MATLAB code from the attached file "GaussDCA-matlab.tgz", or from https://github.com/carlobaldassi/GaussDCA.matlab. See the README.md file for instructions.
This code implements a novel data compression technique for binary symmetric sources based on the cavity method over GF(q), the Galois Field of order q. We present a scheme of low complexity and near-optimal empirical performance. The compression step is based on a reduction of a sparse low-density parity-check code over GF(q) and is done through the so-called reinforced belief-propagation equations. These reduced codes appear to have a nontrivial geometrical modification of the space of codewords, which makes such compression computationally feasible.
This is code for biological data analysis using a multi-state diluted discrete perceptron.
SBPI is an algorithm implementing the extremely simple learning protocol for networks with binary synapses reported in Efficient supervised learning in networks with binary synapses.
The version named SBPI-MPI is a parallelized version which can be run on a cluster, using the MPI parallelization library.
This is the code associated with Learning by message-passing in networks of discrete synapses
This is the Survey Propagation C source code, plus some old revisions. Survey Propagation is a generalization of Belief Propagation that is able to solve huge random SAT formulae close to the SAT/UNSAT threshold.