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In practical application, customizing an image-analysis algorithm or finding a sufficient number of example cells to train a machine learning algorithm can be infeasible, particularly when positive control samples are not available and the phenotype of interest is rare. Here we present a supervised machine learning approach that uses iterative feedback to readily score multiple subtle and complex morphological phenotypes in high-throughput, image-based screens. First, automated cytological profiling extracts hundreds of numerical descriptors for every cell in every image. Next, the researcher generates a rule (i.e., classifier) to recognize cells with a phenotype of interest during a short, interactive training session using iterative feedback. Finally, all of the cells in the experiment are automatically classified and each sample is scored based on the presence of cells displaying the phenotype. By using this approach, we successfully scored images in RNA interference screens in 2 organisms for the prevalence of 15 diverse cellular morphologies, some of which were previously intractable.<\/jats:p>","DOI":"10.1073\/pnas.0808843106","type":"journal-article","created":{"date-parts":[[2009,2,3]],"date-time":"2009-02-03T02:35:01Z","timestamp":1233628501000},"page":"1826-1831","update-policy":"https:\/\/doi.org\/10.1073\/pnas.cm10313","source":"Crossref","is-referenced-by-count":331,"title":["Scoring diverse cellular morphologies in image-based screens with iterative feedback and machine learning"],"prefix":"10.1073","volume":"106","author":[{"given":"Thouis R.","family":"Jones","sequence":"first","affiliation":[{"name":"The Broad Institute of MIT and Harvard, 7 Cambridge Center, Cambridge, MA 02142;"},{"name":"Whitehead Institute for Biomedical Research, Massachusetts Institute of Technology, 9 Cambridge Center, Cambridge, MA 02142;"},{"name":"Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 32 Vassar Street, Cambridge, MA 02139;"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anne E.","family":"Carpenter","sequence":"additional","affiliation":[{"name":"The Broad Institute of MIT and Harvard, 7 Cambridge Center, Cambridge, MA 02142;"},{"name":"Whitehead Institute for Biomedical Research, Massachusetts Institute of Technology, 9 Cambridge Center, Cambridge, MA 02142;"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael R.","family":"Lamprecht","sequence":"additional","affiliation":[{"name":"Whitehead Institute for Biomedical Research, Massachusetts Institute of Technology, 9 Cambridge Center, Cambridge, MA 02142;"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jason","family":"Moffat","sequence":"additional","affiliation":[{"name":"Whitehead Institute for Biomedical Research, Massachusetts Institute of Technology, 9 Cambridge Center, Cambridge, MA 02142;"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Serena J.","family":"Silver","sequence":"additional","affiliation":[{"name":"The Broad Institute of MIT and Harvard, 7 Cambridge Center, Cambridge, MA 02142;"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jennifer K.","family":"Grenier","sequence":"additional","affiliation":[{"name":"The Broad Institute of MIT and Harvard, 7 Cambridge Center, Cambridge, MA 02142;"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Adam B.","family":"Castoreno","sequence":"additional","affiliation":[{"name":"Dana-Farber Cancer Institute and Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, 250 Longwood Avenue, Boston, MA 02115; 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