Nadir Sella

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The ability to reconstruct and find genes that belong or are connected to a gene regulatory network is of essential importance in biology, in order to understand how the biological processes of an organism work. The main limitation in performing gene network expansion is related to the huge amount of computations needed to discover new candidate genes.(More)
Learning causal networks from large-scale genomic data remains challenging in absence of time series or controlled perturbation experiments. We report an information- theoretic method which learns a large class of causal or non-causal graphical models from purely observational data, while including the effects of unobserved latent variables, commonly found(More)
Our group has recently developed gene@home, a BOINC project that permits to search for candidate genes for the expansion of a gene regulatory network using gene expression data. The gene@home project adopts intensive variablesubsetting strategies enabled by the computational power provided by the volunteers who have joined the project by means of the BOINC(More)
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