Statistical Physics

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

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("") in the julia command line. See also the documentation at

Matlab code

You can download the MATLAB code from the attached file "GaussDCA-matlab.tgz", or from See the file for instructions.

The patient-zero problem with noisy observation

The patient-zero problem consists in finding the initial source of an epidemic outbreak given observations at a later time. In this seminar, I will describe a Bayesian method which is able to infer details on the past history of an epidemics based solely on the topology of the contact network and a single snapshot of partial and noisy observations. The method is built on a Bethe approximation for the posterior distribution, and is inherently exact on tree graphs. Moreover, it can be coupled to a set of equations, based on the variational expression of the Bethe free energy, to find the patient-zero along with maximum-likelihood epidemic parameters.
I will describe the method and some results for simulated epidemics on random graphs, and briefly mention future directions of research in the discrete-time setting, as well as a new method - currently in the test phase - that can perform inference on a continuous time spreading model and deal efficiently with real contact-time data.

Thu, 30/10/2014 - 14:30
Alessandro Ingrosso
HuGeF, Via Nizza 52

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

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

Signal localization as a phase separation process

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Thu, 16/01/2014 - 14:30
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Spatial disorder in the Voter Model

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Wed, 12/12/2012 - 15:00
Claudio Borile
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