{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["17(Suppl 16)"],"submitter":["Affeldt S"],"pubmed_abstract":["<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 th"],"journal":["BMC bioinformatics"],"pagination":["493"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC5249011"],"repository":["biostudies-literature"],"pubmed_title":["Spectral consensus strategy for accurate reconstruction of large biological networks."],"pmcid":["PMC5249011"],"pubmed_authors":["Sokolovska N","Zucker JD","Affeldt S","Prifti E"],"additional_accession":[]},"is_claimable":false,"name":"Spectral consensus strategy for accurate reconstruction of large biological networks.","description":"<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 th","dates":{"release":"2016-01-01T00:00:00Z","publication":"2016 Dec","modification":"2026-07-15T22:07:29.316Z","creation":"2026-07-09T10:20:33.678Z"},"accession":"S-EPMC5249011","cross_references":{"pubmed":["28105915"],"doi":["10.1186/s12859-016-1308-y"]}}