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Learning directed acyclic graphs from large-scale genomics data

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

Copyright Information: CC BY 4.0 International - Creative Commons, Attribution.

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Item Type: Article
Type of entry: Secondary publication
Title: Learning directed acyclic graphs from large-scale genomics data
Language: English
Date: 2017
Place of Publication: Darmstadt
Year of primary publication: 2017
Publisher: Springer Open
Journal or Publication Title: EURASIP Journal on Bioinformatics and Systems Biology
Volume of the journal: 2017
Issue Number: 10
Corresponding Links:
Origin: Secondary publication via sponsored Golden Open Access

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.

URN: urn:nbn:de:tuda-tuprints-68294
Classification DDC: 600 Technology, medicine, applied sciences > 600 Technology
Divisions: 18 Department of Electrical Engineering and Information Technology > Institute for Telecommunications > Communication Systems
Date Deposited: 26 Sep 2017 13:20
Last Modified: 13 Dec 2022 11:05
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/6829
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