%0 Journal Article
%9 ACL : Articles dans des revues avec comité de lecture répertoriées par l'AERES
%A Affeldt, S.
%A Sokolovska, N.
%A Prifti, E.
%A Zucker, Jean-Daniel
%T Spectral consensus strategy for accurate reconstruction of large biological networks
%D 2016
%L fdi:010068895
%G ENG
%J BMC Bioinformatics
%@ 1471-2105
%K Network reconstruction ; Community-based method ; Spectral theory ; High-dimensional data ; Microbiota
%M ISI:000392601400007
%N 16
%P art. 493 [13 ]
%R 10.1186/s12859-016-1308-y
%U https://www.documentation.ird.fr/hor/fdi:010068895
%> https://horizon.documentation.ird.fr/exl-doc/pleins_textes/divers17-02/010068895.pdf
%V 17
%W Horizon (IRD)
%X Background: The last decades witnessed an explosion of large-scale biological datasets whose analyses require the continuous development of innovative algorithms. Many of these high-dimensional datasets are related to large biological networks with few or no experimentally proven interactions. A striking example lies in the recent gut bacterial studies that provided researchers with a plethora of information sources. Despite a deeper knowledge of microbiome composition, inferring bacterial interactions remains a critical step that encounters significant issues, due in particular to high-dimensional settings, unknown gut bacterial taxa and unavoidable noise in sparse datasets. Such data type make any a priori choice of a learning method particularly difficult and urge the need for the development of new scalable approaches. Results: We propose a consensus method based on spectral decomposition, named Spectral Consensus Strategy, to reconstruct large networks from high-dimensional datasets. This novel unsupervised approach can be applied to a broad range of biological networks and the associated spectral framework provides scalability to diverse reconstruction methods. The results obtained on benchmark datasets demonstrate the interest of our approach for high-dimensional cases. As a suitable example, we considered the human gut microbiome co-presence network. For this application, our method successfully retrieves biologically relevant relationships and gives new insights into the topology of this complex ecosystem. Conclusions: The Spectral Consensus Strategy improves prediction precision and allows scalability of various reconstruction methods to large networks. The integration of multiple reconstruction algorithms turns our approach into a robust learning method. All together, this strategy increases the confidence of predicted interactions from high-dimensional datasets without demanding computations.
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