code

Source code

GaussDCA: Multivariate Gaussian Inference of Protein Contacts from Multiple Sequence Alignment

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GaussDCA-julia.tgz25.09 KB
GaussDCA-matlab.tgz24.36 KB

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

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 code

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 Pkg.clone("https://github.com/carlobaldassi/GaussDCA.jl") in the julia command line. See also the documentation at https://github.com/carlobaldassi/GaussDCA.jl.

Matlab code

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.

RBP algorithm for lossy compression in reduced, ultrasparse GF(q) codes

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gf-rbp.tgz24.36 KB

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.

Prize-Collecting Steiner Trees

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msgsteiner.tgz6.84 KB
msgsteiner-1.1.tgz10.35 KB

This is the distribution package of MSGSTEINER

The permission to use this software is granted by the authors, Alfredo
Braunstein and Riccardo Zecchina, only on the following 5 conditions:

BP for the diluted perceptron

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dilperc.tgz14.1 KB

This is code for biological data analysis using a multi-state diluted discrete perceptron.
It contains also a matlab mex implementation.

SBPI for binary perceptron learning

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SBPI-MPI.tgz16.15 KB
SBPI.tgz15.65 KB

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.

Survey Propagation

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sp-1.1.tgz26.82 KB
sp-1.2.tgz27.79 KB
sp-1.3.tgz27.8 KB
sp-1.4b.tgz28.23 KB

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.

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