topology

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.

A cavity-method based approach to the Steiner tree problem on graphs

The minimum weight Steiner tree problem (MST) is an important combinatorial optimization problem over networks that has applications in a wide range of fields. I will mainly focus my attention on two variants of the problem:
the Vertex-disjoint Steiner trees problem and Edges-disjoint Steiner trees problem on graphs. For each variant I will propose some efficient algorithms based on the Belief Propagation approximation scheme.

Date: 
Thu, 23/10/2014 - 14:30
Speaker: 
Anna Paola Muntoni
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