Sulaimanov, Nurgazy ; Koeppl, Heinz (2016)
Graph reconstruction using covariance-based methods.
In: EURASIP Journal on Bioinformatics and Systems Biology
doi: 10.1186/s13637-016-0052-y
Artikel, Bibliographie
Dies ist die neueste Version dieses Eintrags.
Kurzbeschreibung (Abstract)
Methods based on correlation and partial correlation are today employed in the reconstruction of a statistical interaction graph from high-throughput omics data. These dedicated methods work well even for the case when the number of variables exceeds the number of samples. In this study, we investigate how the graphs extracted from covariance and concentration matrix estimates are related by using Neumann series and transitive closure and through discussing concrete small examples. Considering the ideal case where the true graph is available, we also compare correlation and partial correlation methods for large realistic graphs. In particular, we perform the comparisons with optimally selected parameters based on the true underlying graph and with data-driven approaches where the parameters are directly estimated from the data.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2016 |
| Autor(en): | Sulaimanov, Nurgazy ; Koeppl, Heinz |
| Art des Eintrags: | Bibliographie |
| Titel: | Graph reconstruction using covariance-based methods |
| Sprache: | Englisch |
| Publikationsjahr: | 2016 |
| Ort: | Heidelberg |
| Verlag: | Springer |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | EURASIP Journal on Bioinformatics and Systems Biology |
| Kollation: | 20 Seiten |
| DOI: | 10.1186/s13637-016-0052-y |
| Zugehörige Links: | |
| Kurzbeschreibung (Abstract): | Methods based on correlation and partial correlation are today employed in the reconstruction of a statistical interaction graph from high-throughput omics data. These dedicated methods work well even for the case when the number of variables exceeds the number of samples. In this study, we investigate how the graphs extracted from covariance and concentration matrix estimates are related by using Neumann series and transitive closure and through discussing concrete small examples. Considering the ideal case where the true graph is available, we also compare correlation and partial correlation methods for large realistic graphs. In particular, we perform the comparisons with optimally selected parameters based on the true underlying graph and with data-driven approaches where the parameters are directly estimated from the data. |
| Freie Schlagworte: | High-dimensional graph reconstruction methods, Concentration and covariance graphs |
| ID-Nummer: | Artikel-ID: 19 |
| Zusätzliche Informationen: | Erstveröffentlichung |
| Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 500 Naturwissenschaften und Mathematik > 510 Mathematik 500 Naturwissenschaften und Mathematik > 570 Biowissenschaften, Biologie 600 Technik, Medizin, angewandte Wissenschaften > 621.3 Elektrotechnik, Elektronik |
| Fachbereich(e)/-gebiet(e): | 18 Fachbereich Elektrotechnik und Informationstechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik > Bioinspirierte Kommunikationssysteme 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Self-Organizing Systems Lab Interdisziplinäre Forschungsprojekte Interdisziplinäre Forschungsprojekte > Centre for Synthetic Biology |
| Hinterlegungsdatum: | 08 Mai 2024 11:57 |
| Letzte Änderung: | 08 Mai 2024 11:57 |
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| Suche nach Titel in: | TUfind oder in Google |
Verfügbare Versionen dieses Eintrags
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Graph reconstruction using covariance-based methods. (deposited 30 Apr 2024 09:30)
- Graph reconstruction using covariance-based methods. (deposited 08 Mai 2024 11:57) [Gegenwärtig angezeigt]
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