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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
Article, Bibliographie

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.

Item Type: Article
Erschienen: 2016
Creators: Sulaimanov, N. ; Koeppl, H.
Type of entry: Bibliographie
Title: Graph reconstruction using covariance based methods
Language: English
Date: November 2016
Publisher: Springer
Journal or Publication Title: EURASIP Journal on Bioinformatics and Systems Biology
URL / URN: http://bsb.eurasipjournals.springeropen.com/articles/10.1186...
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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.

Divisions: 18 Department of Electrical Engineering and Information Technology
18 Department of Electrical Engineering and Information Technology > Institute for Telecommunications > Bioinspired Communication Systems
18 Department of Electrical Engineering and Information Technology > Institute for Telecommunications
Date Deposited: 02 Sep 2016 06:28
Last Modified: 23 Sep 2021 14:31
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