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.
Learning directed-acyclic-graphs from multiple genomic data sources [pdf]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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