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Though some biological processes commonly occur across contexts, by harnessing the vast amounts of available gene expression data, we can decipher the processes that are unique to a specific context. Therefore, with the goal of developing a portrait of context-specific patterns to better elucidate how they govern distinct biological processes, this work presents a large-scale exploration of transcriptomic signatures across three different contexts (i.e., tissues, cell types, and cell lines) by leveraging over 600 gene expression datasets categorized into 98 subcontexts. The strongest pairwise correlations between genes from these subcontexts are used for the construction of co-expression networks. Using a network-based approach, we then pinpoint patterns that are unique and common across these subcontexts. First, we focused on patterns at the level of individual nodes and evaluated their functional roles using a human protein\u2013protein interactome as a referential network. Next, within each context, we systematically overlaid the co-expression networks to identify specific and shared correlations as well as relations already described in scientific literature. Additionally, in a pathway-level analysis, we overlaid node and edge sets from co-expression networks against pathway knowledge to identify biological processes that are related to specific subcontexts or groups of them. Finally, we have released our data and scripts at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/zenodo.org\/record\/5831786\">https:\/\/zenodo.org\/record\/5831786<\/jats:ext-link>\n                    and\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/ContNeXt\/\">https:\/\/github.com\/ContNeXt\/<\/jats:ext-link>\n                    , respectively and developed ContNeXt (\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/contnext.scai.fraunhofer.de\/\">https:\/\/contnext.scai.fraunhofer.de\/<\/jats:ext-link>\n                    ), a web application to explore the networks generated in this work.\n                  <\/jats:p>","DOI":"10.1186\/s12859-022-04765-0","type":"journal-article","created":{"date-parts":[[2022,6,15]],"date-time":"2022-06-15T08:03:18Z","timestamp":1655280198000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Elucidating gene expression patterns across multiple biological contexts through a large-scale investigation of transcriptomic datasets"],"prefix":"10.1186","volume":"23","author":[{"given":"Rebeca Queiroz","family":"Figueiredo","sequence":"first","affiliation":[]},{"given":"Sara D\u00edaz","family":"del Ser","sequence":"additional","affiliation":[]},{"given":"Tamara","family":"Raschka","sequence":"additional","affiliation":[]},{"given":"Martin","family":"Hofmann-Apitius","sequence":"additional","affiliation":[]},{"given":"Alpha Tom","family":"Kodamullil","sequence":"additional","affiliation":[]},{"given":"Sarah","family":"Mubeen","sequence":"additional","affiliation":[]},{"given":"Daniel","family":"Domingo-Fern\u00e1ndez","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,6,15]]},"reference":[{"issue":"1","key":"4765_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41540-021-00186-6","volume":"7","author":"T Azevedo","year":"2021","unstructured":"Azevedo T, Dimitri GM, Li\u00f3 P, Gamazon ER. 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