Sulaimanov, N. ; Koeppl, H. (2016)
Graph reconstruction using covariance based methods.
In: EURASIP Journal on Bioinformatics and Systems Biology
Artikel, Bibliographie
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, N. ; Koeppl, H. |
Art des Eintrags: | Bibliographie |
Titel: | Graph reconstruction using covariance based methods |
Sprache: | Englisch |
Publikationsjahr: | November 2016 |
Verlag: | Springer |
Titel der Zeitschrift, Zeitung oder Schriftenreihe: | EURASIP Journal on Bioinformatics and Systems Biology |
URL / URN: | http://bsb.eurasipjournals.springeropen.com/articles/10.1186... |
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. |
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 |
Hinterlegungsdatum: | 02 Sep 2016 06:28 |
Letzte Änderung: | 23 Sep 2021 14:31 |
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