Reconstruction and clustering with graph optimization and priors on gene networks and images (2017)
Abstract / truncated to 115 words
The discovery of novel gene regulatory processes improves the understanding of cell phenotypic responses to external stimuli for many biological applications, such as medicine, environment or biotechnologies. To this purpose, transcriptomic data are generated and analyzed from DNA microarrays or more recently RNAseq experiments. They consist in genetic expression level sequences obtained for all genes of a studied organism placed in different living conditions. From these data, gene regulation mechanisms can be recovered by revealing topological links encoded in graphs. In regulatory graphs, nodes correspond to genes. A link between two nodes is identified if a regulation relationship exists between the two corresponding genes. Such networks are called Gene Regulatory Networks (GRNs). Their construction as ... toggle 19 keywordsgene network inference – clustering – data science – graph optimization – maximal flow – minimum cut – random walker algorithm – variational and bayes variational formalism – convex relaxation – alternating optimization – combinatorial dirichlet problem – hard-clustering and soft-clustering – biology – biotechnology – bioinformatics – transcription factors – modular networks – biological priors – in-silico
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