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Graph reconstruction using covariance based methods

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...
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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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