TU Darmstadt / ULB / TUbiblio

Learning directed acyclic graphs from large-scale genomics data

Nikolay, Fabio and Pesavento, Marius and Kritikos, George and Typas, Nassos (2017):
Learning directed acyclic graphs from large-scale genomics data.
In: EURASIP Journal on Bioinformatics and Systems Biology, Springer Open, 2017, (10), ISSN 1687-4153, DOI: 10.1186/s13637-017-0063-3, [Online-Edition: https://bsb-eurasipjournals.springeropen.com/track/pdf/10.11...],
[Article]

Abstract

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.

Item Type: Article
Erschienen: 2017
Creators: Nikolay, Fabio and Pesavento, Marius and Kritikos, George and Typas, Nassos
Title: Learning directed acyclic graphs from large-scale genomics data
Language: English
Abstract:

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
Divisions: 18 Department of Electrical Engineering and Information Technology > Institute for Telecommunications > Communication Systems
18 Department of Electrical Engineering and Information Technology > Institute for Telecommunications
18 Department of Electrical Engineering and Information Technology
Date Deposited: 01 Oct 2017 19:55
DOI: 10.1186/s13637-017-0063-3
Official URL: https://bsb-eurasipjournals.springeropen.com/track/pdf/10.11...
URN: urn:nbn:de:tuda-tuprints-68294
Export:

Optionen (nur für Redakteure)

View Item View Item