Learning directed-acyclic-graphs from multiple genomic data sources

In this paper we consider the problem of learning the topology of a directed-acyclic-graph, that describes the interactions among a set of genes, based on noisy double knockout data and genetic-interactions-profile data. We propose a novel linear integer optimization approach to identify the complex biological dependencies among genes and to compute the topology of the directed-acyclic-graph that matches the data best. Finally, we apply a sequential scalability technique for large sets of genes along with our proposed algorithm, in order to provide statistically significant results for experimental data.