biostudies-literatureUnknown17(Suppl 16)Affeldt S<h4>Background</h4>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 thBMC bioinformatics493https://www.ebi.ac.uk/biostudies/studies/S-EPMC5249011biostudies-literatureSpectral consensus strategy for accurate reconstruction of large biological networks.PMC5249011Sokolovska NZucker JDAffeldt SPrifti EfalseSpectral consensus strategy for accurate reconstruction of large biological networks.<h4>Background</h4>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 th2016-01-01T00:00:00Z2016 Dec2026-07-15T22:07:29.316Z2026-07-09T10:20:33.678ZS-EPMC52490112810591510.1186/s12859-016-1308-y