{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T23:51:44Z","timestamp":1789775504896,"version":"4.0.1"},"reference-count":39,"publisher":"Oxford University Press (OUP)","issue":"7","license":[{"start":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T00:00:00Z","timestamp":1783900800000},"content-version":"vor","delay-in-days":12,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Plant Science Group"},{"name":"departments of Mathematical and Statistical Methods (Biometris) and of Cell and Developmental Biology at Wageningen University and Research","award":["3183500140"],"award-info":[{"award-number":["3183500140"]}]},{"name":"NWO VIDI","award":["VI.Vidi.193.119"],"award-info":[{"award-number":["VI.Vidi.193.119"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,7,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>In recent years, the availability of multi-omics data has increased substantially. Multi-omics data integration methods mainly aim to leverage different molecular layers to gain a complete molecular description of biological processes. An attractive integration approach is the reconstruction of multi-omics networks. However, the development of effective multi-omics network reconstruction strategies lags behind.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In this study, we introduce collaborative graphical lasso, a novel approach that extends graphical lasso by incorporating collaboration between omics layers, thereby improving multi-omics data integration and enhancing network inference. Our method leverages a collaborative penalty term, which harmonizes the contribution of the omics layers to the reconstruction of the network structure. This promotes a cohesive integration of information across modalities, and it is introduced alongside a dual regularization scheme that separately controls sparsity within and between layers. To address the challenge of model selection in this framework, we propose XStARS, a stability-based criterion for multi-dimensional hyperparameter tuning. We assess the performance of collaborative graphical lasso and the corresponding model selection procedure through simulations, and we apply them to publicly available multi-omics data. This application demonstrated collaborative graphical lasso recovers established biological interactions while suggesting novel, biologically coherent connections.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>We implemented collaborative graphical lasso as an R package, available on CRAN as coglasso. The results of the manuscript can be reproduced running the code available at https:\/\/github.com\/DrQuestion\/coglasso_reproducible_code, deposited on figshare with DOI: https:\/\/doi.org\/10.6084\/m9.figshare.32324376.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btag477","type":"journal-article","created":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T11:27:09Z","timestamp":1783942029000},"source":"Crossref","is-referenced-by-count":2,"title":["Multi-omics network reconstruction with\n                    <i>collaborative graphical lasso<\/i>"],"prefix":"10.1093","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1783-5613","authenticated-orcid":false,"given":"Alessio","family":"Albanese","sequence":"first","affiliation":[{"name":"Mathematical and Statistical Methods Group\u2014Biometris, Wageningen University and Research , Wageningen 6700AA,","place":["The Netherlands"]},{"name":"Cell and Developmental Biology, Wageningen University 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