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Learning directed acyclic graphs from large-scale genomics data

Nikolay, Fabio and Pesavento, Marius and Kritikos, George and Typas, Nassos :
Learning directed acyclic graphs from large-scale genomics data.
In: EURASIP Journal on Bioinformatics and Systems Biology, 2017 (10) ISSN 1687-4153
[Article], (2017)

Available under CC-BY 4.0 International - Creative Commons, Attribution.

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Item Type: Article
Title: Learning directed acyclic graphs from large-scale genomics data
Language: English

In this paper, we consider the problem of learning the genetic interaction map, i.e., the topology of a directed acyclic graph (DAG) of genetic interactions from noisy double-knockout (DK) data. Based on a set of well-established biological interaction models, we detect and classify the interactions between genes. We propose a novel linear integer optimization program called the Genetic-Interactions-Detector (GENIE) to identify the complex biological dependencies among genes and to compute the DAG topology that matches the DK measurements best. Furthermore, we extend the GENIE program by incorporating genetic interaction profile (GI-profile) data to further enhance the detection performance. In addition, we propose a sequential scalability technique for large sets of genes under study, in order to provide statistically significant results for real measurement data. Finally, we show via numeric simulations that the GENIE program and the GI-profile data extended GENIE (GI-GENIE) program clearly outperform the conventional techniques and present real data results for our proposed sequential scalability technique.

Journal or Publication Title: EURASIP Journal on Bioinformatics and Systems Biology
Volume: 2017
Number: 10
Publisher: Springer Open
Classification DDC: 600 Technik, Medizin, angewandte Wissenschaften > 600 Technik
Divisions: 18 Department of Electrical Engineering and Information Technology > Institute for Telecommunications > Communication Systems
Date Deposited: 26 Sep 2017 13:20
Last Modified: 26 Sep 2017 13:30
DOI: 10.1186/s13637-017-0063-3
Official URL: https://doi.org/10.1186/s13637-017-0063-3
URN: urn:nbn:de:tuda-tuprints-68294
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/6829
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