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optimizationCombinatorial Optimization 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. The patientzero problem with noisy observationThe patientzero 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 patientzero along with maximumlikelihood epidemic parameters. Date:
Thu, 30/10/2014  14:30
Speaker:
Alessandro Ingrosso
Place:
HuGeF, Via Nizza 52
A cavitymethod based approach to the Steiner tree problem on graphsThe 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: Date:
Thu, 23/10/2014  14:30
Speaker:
Anna Paola Muntoni
Optimizing spread dynamics on graphs by message passing
Optimizing spread dynamics on graphs by message passing. Journal of Statistical Mechanics: Theory and Experiment. 2013;2013(09):P09011.
Large deviations of cascade processes on graphs
Large deviations of cascade processes on graphs. Phys. Rev. E. 2013;87:062115.
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 Aligning graphs and finding substructures by a cavity approach
Aligning graphs and finding substructures by a cavity approach. EPL (Europhysics Letters). 2010;89:37009.
Stochastic optimization by message passing
Stochastic optimization by message passing. JOURNAL OF STATISTICAL MECHANICS: THEORY AND EXPERIMENT. 2011;2011.
A PrizeCollecting Steiner Tree Approach for Transduction Network Inference
A PrizeCollecting Steiner Tree Approach for Transduction Network Inference. In: Proceedings of the 7th International Conference on Computational Methods in Systems Biology. Springer; 2009. 95.
