codeSource code GaussDCA: Multivariate Gaussian Inference of Protein Contacts from Multiple Sequence Alignment
This is the code which accompanies the paper "Fast and accurate multivariate Gaussian modeling of protein families: Predicting residue contacts and proteininteraction 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 The code comes in two versions, one for Julia and one for MATLAB. They provide nearly identical funcitionality. The Julia code is slightly faster, and doesn't require compilation of external modules. Julia codeYou can download the Julia code from the attached file "GaussDCAjulia.tgz"; however, the recommended way to obtain the code is by using the command Matlab codeYou can download the MATLAB code from the attached file "GaussDCAmatlab.tgz", or from https://github.com/carlobaldassi/GaussDCA.matlab. See the README.md file for instructions. RBP algorithm for lossy compression in reduced, ultrasparse GF(q) codes
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 nearoptimal empirical performance. The compression step is based on a reduction of a sparse lowdensity paritycheck code over GF(q) and is done through the socalled reinforced beliefpropagation equations. These reduced codes appear to have a nontrivial geometrical modification of the space of codewords, which makes such compression computationally feasible.
PrizeCollecting Steiner Trees
This is the distribution package of MSGSTEINER The permission to use this software is granted by the authors, Alfredo BP for the diluted perceptron
This is code for biological data analysis using a multistate diluted discrete perceptron. SBPI for binary perceptron learning
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 SBPIMPI is a parallelized version which can be run on a cluster, using the MPI parallelization library.
BP for binary perceptron learning
This is the code associated with Learning by messagepassing in networks of discrete synapses
Survey Propagation
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.
Survey Propagation for energy minimization
