Gene regulatory network expansion is a task of the foremost importance in computational biology that aims at finding new genes to expand a given known gene regulatory network. To this end we present OneGenE, a novel f...
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ISBN:
(纸本)9781728116440
Gene regulatory network expansion is a task of the foremost importance in computational biology that aims at finding new genes to expand a given known gene regulatory network. To this end we present OneGenE, a novel framework for gene regulatory network expansion that relies on the BOINC platform. OneGenE is an evolution of the NES2RA algorithm, with the aim to overcome its main criticality, i.e. long response time for the final user. To achieve this goal, candidate expansion lists are pre-computed for each gene in the organism and then aggregated at runtime to produce the the final expansion list for a given known gene regulatory network. We validated OneGenE on the expression data of Pseudomonas aeruginosa, comparing its results with the one obtained by NES2RA and through a biological literature review.
The automatic discovery of causal relationships among human genes can shed light on gene regulatory processes and guide drug repositioning. To this end, a computationally-heavy method for causal discovery is distribut...
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The automatic discovery of causal relationships among human genes can shed light on gene regulatory processes and guide drug repositioning. To this end, a computationally-heavy method for causal discovery is distributed on a volunteercomputing grid and, taking advantage of variable subsetting and stratification, proves to be useful for expanding local gene regulatory networks. The input data are purely observational measures of transcripts expression in human tissues and cell lines collected within the FANTOM project. The system relies on the BOINC platform and on optimized client code. The functional relevance of results, measured by analyzing the annotations of the identified interactions, increases significantly over the simple Pearson correlation between the transcripts. Additionally, in 82 percent of cases networks significantly overlap with known protein-protein interactions annotated in biological databases. In the two case studies presented, this approach has been used to expand the networks of genes associated with two severe human pathologies: prostate cancer and coronary artery disease. The method identified respectively 22 and 36 genes to be evaluated as novel targets for already approved drugs, demonstrating the effective applicability of the approach in pipelines aimed to drug repositioning.
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