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Such findings could support refined biological insight and hypothesis generation. However, standard analytical approaches are not designed to be \u201come aware.\u201d Thus, some researchers analyze data from one ome at a time, and then combine predictions across omes. Others resort to correlation studies, cataloging pairwise relationships, but lacking an obvious approach for cohesive and interpretable summaries of these catalogs.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We present a novel workflow for building predictive regression models from network neighborhoods in multi-omic networks. First, we generate pairwise regression models across all pairs of analytes from all omes, encoding the resulting \u201ctop table\u201d of relationships in a network. Then, we build predictive logistic regression models using the analytes in network neighborhoods of interest. We call this method CANTARE (Consolidated Analysis of Network Topology And Regression Elements).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We applied CANTARE to previously published data from healthy controls and patients with inflammatory bowel disease (IBD) consisting of three omes: gut microbiome, metabolomics, and microbial-derived enzymes. We identified 8 unique predictive models with AUC\u2009&gt;\u20090.90. The number of predictors in these models ranged from 3 to 13. We compare the results of CANTARE to random forests and elastic-net penalized regressions, analyzing AUC, predictions, and predictors. CANTARE AUC values were competitive with those generated by random forests and\u00a0\u00a0penalized regressions. The top 3 CANTARE models had a greater dynamic range of predicted probabilities than did random forests and penalized regressions (p-value\u2009=\u20091.35\u2009\u00d7\u200910<jats:sup>\u20135<\/jats:sup>). CANTARE models were significantly more likely to prioritize predictors from multiple omes than were the alternatives (p-value\u2009=\u20090.005). We also showed that predictive models from a network based on pairwise models with an interaction term for IBD have higher AUC than predictive models built from a correlation network (p-value\u2009=\u20090.016). R scripts and a CANTARE User\u2019s Guide are available at<jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/sourceforge.net\/projects\/cytomelodics\/files\/CANTARE\/\">https:\/\/sourceforge.net\/projects\/cytomelodics\/files\/CANTARE\/<\/jats:ext-link>.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>CANTARE offers a flexible approach for building parsimonious, interpretable multi-omic models. These models yield quantitative and directional effect sizes for predictors and support the generation of hypotheses for follow-up investigation.<\/jats:p><\/jats:sec>","DOI":"10.1186\/s12859-021-04016-8","type":"journal-article","created":{"date-parts":[[2021,2,20]],"date-time":"2021-02-20T23:02:47Z","timestamp":1613862167000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["CANTARE: finding and visualizing network-based multi-omic predictive models"],"prefix":"10.1186","volume":"22","author":[{"given":"Janet C.","family":"Siebert","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Martine","family":"Saint-Cyr","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sarah J.","family":"Borengasser","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Brandie D.","family":"Wagner","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Catherine A.","family":"Lozupone","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Carsten","family":"G\u00f6rg","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,2,19]]},"reference":[{"key":"4016_CR1","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1093\/bioinformatics\/bty537","volume":"35","author":"MS Ghaemi","year":"2019","unstructured":"Ghaemi MS, DiGiulio DB, Contrepois K, Callahan B, Ngo TTM, Lee-McMullen B, et al. 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