{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T18:54:33Z","timestamp":1774378473874,"version":"3.50.1"},"reference-count":17,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T00:00:00Z","timestamp":1654041600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T00:00:00Z","timestamp":1654041600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"published-print":{"date-parts":[[2022,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>\n                      Bioinformatics investigators often gain insights by combining information across multiple and disparate data sets. Merging data from multiple sources frequently results in data sets that are incomplete or contain missing values. Although missing data are ubiquitous, existing implementations of Gaussian mixture models (GMMs) either cannot accommodate missing data, or do so by imposing simplifying assumptions that limit the applicability of the model. In the presence of missing data, a standard\n                      <jats:italic>ad hoc<\/jats:italic>\n                      practice is to perform complete case analysis or imputation prior to model fitting. Both approaches have serious drawbacks, potentially resulting in biased and unstable parameter estimates.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Here we present missingness-aware Gaussian mixture models (), an package for fitting GMMs in the presence of missing data. Unlike existing GMM implementations that can accommodate missing data, places no restrictions on the form of the covariance matrix. Using three case studies on real and simulated\n                      <jats:italic>\u2019omics<\/jats:italic>\n                      data sets, we demonstrate that, when the underlying data distribution is near-to a GMM, is more effective at recovering the true cluster assignments than either the existing GMM implementations that accommodate missing data, or fitting a standard GMM after state of the art imputation. Moreover, provides an accurate assessment of cluster assignment uncertainty, even when the generative distribution is not a GMM.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>\n                      Compared to state-of-the-art competitors, demonstrates a better ability to recover the true cluster assignments for a wide variety of data sets and a large range of missingness rates. provides the bioinformatics community with a powerful, easy-to-use, and statistically sound tool for performing clustering and density estimation in the presence of missing data. is publicly available as an package on CRAN:\n                      <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/CRAN.R-project.org\/package=MGMM\">https:\/\/CRAN.R-project.org\/package=MGMM<\/jats:ext-link>\n                      .\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12859-022-04740-9","type":"journal-article","created":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T06:03:26Z","timestamp":1654063406000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Fitting Gaussian mixture models on incomplete data"],"prefix":"10.1186","volume":"23","author":[{"given":"Zachary R.","family":"McCaw","sequence":"first","affiliation":[]},{"given":"Hugues","family":"Aschard","sequence":"additional","affiliation":[]},{"given":"Hanna","family":"Julienne","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,6,1]]},"reference":[{"key":"4740_CR1","volume-title":"Machine learning: a probabilistic perspective","author":"KP Murphy","year":"2012","unstructured":"Murphy KP. 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