Learning directed-acyclic-graphs from multiple genomic data sources. Nikolay, F. & Pesavento, M. In *2017 25th European Signal Processing Conference (EUSIPCO)*, pages 1877-1881, Aug, 2017.

Paper doi abstract bibtex

Paper doi abstract bibtex

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.

@InProceedings{8081535, author = {F. Nikolay and M. Pesavento}, booktitle = {2017 25th European Signal Processing Conference (EUSIPCO)}, title = {Learning directed-acyclic-graphs from multiple genomic data sources}, year = {2017}, pages = {1877-1881}, abstract = {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.}, keywords = {directed graphs;genetics;genomics;integer programming;learning (artificial intelligence);mathematics computing;multiple genomic data sources;genes;noisy double knockout data;genetic-interactions-profile data;directed-acyclic-graph learning;topology;linear integer optimization approach;complex biological dependencies;sequential scalability;Topology;Microorganisms;Europe;Signal processing;Genomics;Bioinformatics;Gene networks;discrete optimization;big data;graph learning}, doi = {10.23919/EUSIPCO.2017.8081535}, issn = {2076-1465}, month = {Aug}, url = {https://www.eurasip.org/proceedings/eusipco/eusipco2017/papers/1570345193.pdf}, }

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