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)
doi: 10.1186/s13637-017-0063-3
Artikel, Zweitveröffentlichung, Verlagsversion
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Kurzbeschreibung (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.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2017 |
| Autor(en): | Nikolay, Fabio ; Pesavento, Marius ; Kritikos, George ; Typas, Nassos |
| Art des Eintrags: | Zweitveröffentlichung |
| Titel: | Learning directed acyclic graphs from large-scale genomics data |
| Sprache: | Englisch |
| Publikationsjahr: | 2017 |
| Publikationsdatum der Erstveröffentlichung: | 2017 |
| Verlag: | Springer Open |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | EURASIP Journal on Bioinformatics and Systems Biology |
| Jahrgang/Volume einer Zeitschrift: | 2017 |
| (Heft-)Nummer: | 10 |
| DOI: | 10.1186/s13637-017-0063-3 |
| URL / URN: | https://bsb-eurasipjournals.springeropen.com/track/pdf/10.11... |
| Herkunft: | Zweitveröffentlichung aus gefördertem Golden Open Access |
| Kurzbeschreibung (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. |
| Status: | Verlagsversion |
| URN: | urn:nbn:de:tuda-tuprints-68294 |
| Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 600 Technik, Medizin, angewandte Wissenschaften > 600 Technik |
| Fachbereich(e)/-gebiet(e): | 18 Fachbereich Elektrotechnik und Informationstechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik > Nachrichtentechnische Systeme |
| Hinterlegungsdatum: | 01 Okt 2017 19:55 |
| Letzte Änderung: | 11 Nov 2020 11:30 |
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| Suche nach Titel in: | TUfind oder in Google |
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