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Bioinformatics: Integrating physical interaction networks in the analysis of Complex Diseases

The traditional genome-wide association (GWA) studies have been largely unsuccessful for complex diseases, like cardiovascular disease. They often fail to replicate their results, and the DNA variants that have been found have a low effect on the disease. It is widely believed that this is due to non-additive genetic interactions, where the disease depends on the variance at two or more loci. Considering the vast number of possible genetic interactions, the traditional GWA methodology is not viable; as a consequence most studies ignore genetic interactions.

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Bioinformatics: Study of Cardiovascular Diseases

Integrating physical interaction networks in the analysis of Complex Diseases

Improving the power of statistical tests for genetic interactions

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Bioinformatics: Using machine learning potentials in conformational sampling and selection of protein structures

Modeling of protein structure is a central challenge in structural bioinformatics, and holds the promise not only to identify classes of structure, but also to provide detailed information about the specific structure and biological function of molecules. This is critically important to guide and understand experimental studies: It enables prediction of binding, simulation, and design for a huge set of proteins whose structures have not yet been determined experimentally (or cannot be obtained), and it is a central part of contemporary drug development.

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Bioinformatics: Using predictions to improve predictions of membrane proteins

Machine learning methods have a long history within bioinformatics. Sequence based machine learning methods can roughly be divided into two classes, local and structural. The local methods are trained on information from a fixed length sequence window surrounding a residue. …

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Bioinformatics: VMCMC: a graphical and statistical analysis tool for Markov chain Monte Carlo traces

Analysing the output from Markov chain Monte Carlo (MCMC) computations is a crucial step in many scientific investigations today, and in particular in evolutionary studies where MCMC has proved to be a strong and flexible framework. VMCMC is a convenient tool with two aims: to make phylogenetic MCMC trace analysis of multiple experiments convenient, especially in a HPC environment, and to simplify visualisation of single traces.

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Brain-IT Highlight: D1R-Golf signaling modules segregate into compartments

The development of a large signaling model that takes into consideration the existence of at least two D1R-Golf signaling compartments explains the data pattern. …

 

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Brain-IT Highlight: D1R-Golf signaling modules segregate into compartments

The development of a large signaling model that takes into consideration the existence of at least two D1R-Golf signaling compartments explains the data pattern. …

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Brain-IT Highlight: Workflows for the estimation of model parameters

When modeling subcellular signaling pathways, experimental data are integrated into a precise and structured framework from which it is possible to make predictions that could be tested experimentally. …

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Courtesy of ECMWF

Climate: Computational issues with interaction between the core and parameterized small-scale processes in climate models

Building e.g. the atmospheric part of a global climate model is done in two major steps. First the core is formulated, i.e. defining the equation system, choose numerical methods and calculation grid for the resolved flow. Then, standard idealized tests are performed to assess the performance of the core. …

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Climate: Direct numerical simulation of cloud turbulence and its interaction with cloud drops

The role played by the clouds is fundamental for the atmosphere and the water of the earth. Nowadays our knowledge in cloud dynamics is still so poor that it represents the cause of an amount of uncertainty in climate predictions and in atmospheric circulation models.

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