{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T01:46:33Z","timestamp":1778291193469,"version":"3.51.4"},"reference-count":52,"publisher":"Oxford University Press (OUP)","issue":"D1","license":[{"start":{"date-parts":[[2022,10,19]],"date-time":"2022-10-19T00:00:00Z","timestamp":1666137600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002842","name":"Chiang Mai University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002842","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001348","name":"A*STAR","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001348","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001349","name":"National Medical Research Council","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001349","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005606","name":"Genome Institute of Singapore","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100005606","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,1,6]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Extensive in vitro cancer drug screening datasets have enabled scientists to identify biomarkers and develop machine learning models for predicting drug sensitivity. While most advancements have focused on omics profiles, cancer drug sensitivity scores precalculated by the original sources are often used as-is, without consideration for variabilities between studies. It is well-known that significant inconsistencies exist between the drug sensitivity scores across datasets due to differences in experimental setups and preprocessing methods used to obtain the sensitivity scores. As a result, many studies opt to focus only on a single dataset, leading to underutilization of available data and a limited interpretation of cancer pharmacogenomics analysis. To overcome these caveats, we have developed CREAMMIST (https:\/\/creammist.mtms.dev), an integrative database that enables users to obtain an integrative dose-response curve, to capture uncertainty (or high certainty when multiple datasets well align) across five widely used cancer cell-line drug\u2013response datasets. We utilized the Bayesian framework to systematically integrate all available dose-response values across datasets (&amp;gt;14 millions dose-response data points). CREAMMIST provides easy-to-use statistics derived from the integrative dose-response curves for various downstream analyses such as identifying biomarkers, selecting drug concentrations for experiments, and training robust machine learning models.<\/jats:p>","DOI":"10.1093\/nar\/gkac911","type":"journal-article","created":{"date-parts":[[2022,10,19]],"date-time":"2022-10-19T12:07:18Z","timestamp":1666181238000},"page":"D1242-D1248","source":"Crossref","is-referenced-by-count":15,"title":["CREAMMIST: an integrative probabilistic database for cancer drug response prediction"],"prefix":"10.1093","volume":"51","author":[{"given":"Hatairat","family":"Yingtaweesittikul","sequence":"first","affiliation":[{"name":"Advanced Research Center for Computational Simulation, Faculty of Science, Chiang Mai University , Chiang Mai, Thailand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiaxi","family":"Wu","sequence":"additional","affiliation":[{"name":"Genome Institute of Singapore, A*STAR , Singapore, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aanchal","family":"Mongia","sequence":"additional","affiliation":[{"name":"Genome Institute of Singapore, A*STAR , Singapore, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rafael","family":"Peres","sequence":"additional","affiliation":[{"name":"Genome Institute of Singapore, A*STAR , Singapore, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Karrie","family":"Ko","sequence":"additional","affiliation":[{"name":"Genome Institute of Singapore, A*STAR , Singapore, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Niranjan","family":"Nagarajan","sequence":"additional","affiliation":[{"name":"Genome Institute of Singapore, A*STAR , Singapore, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4777-4586","authenticated-orcid":false,"given":"Chayaporn","family":"Suphavilai","sequence":"additional","affiliation":[{"name":"Genome Institute of Singapore, A*STAR , Singapore, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2022,10,19]]},"reference":[{"key":"2023010804312144300_B1","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/S0092-8674(00)81683-9","article-title":"The hallmarks of cancer","volume":"100","author":"Hanahan","year":"2000","journal-title":"Cell"},{"key":"2023010804312144300_B2","doi-asserted-by":"crossref","first-page":"646","DOI":"10.1016\/j.cell.2011.02.013","article-title":"Hallmarks of cancer: the next generation","volume":"144","author":"Hanahan","year":"2011","journal-title":"Cell"},{"key":"2023010804312144300_B3","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1158\/2159-8290.CD-21-1059","article-title":"Hallmarks of cancer: new dimensions","volume":"12","author":"Hanahan","year":"2022","journal-title":"Cancer Discov."},{"key":"2023010804312144300_B4","doi-asserted-by":"crossref","first-page":"1219","DOI":"10.1038\/aps.2015.92","article-title":"Intra-tumor heterogeneity of cancer cells and its implications for cancer treatment","volume":"36","author":"Sun","year":"2015","journal-title":"Acta Pharmacol. 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