{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,12]],"date-time":"2026-04-12T08:21:00Z","timestamp":1775982060186,"version":"3.50.1"},"reference-count":23,"publisher":"Oxford University Press (OUP)","issue":"14","license":[{"start":{"date-parts":[[2019,7,8]],"date-time":"2019-07-08T00:00:00Z","timestamp":1562544000000},"content-version":"vor","delay-in-days":7,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"Genome Canada and Genome Quebec"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,7,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>The efficacy of a chemical compound is often tested through dose\u2013response experiments from which efficacy metrics, such as the IC50, can be derived. The Marquardt\u2013Levenberg algorithm (non-linear regression) is commonly used to compute estimations for these metrics. The analysis are however limited and can lead to biased conclusions. The approach does not evaluate the certainty (or uncertainty) of the estimates nor does it allow for the statistical comparison of two datasets. To compensate for these shortcomings, intuition plays an important role in the interpretation of results and the formulations of conclusions. We here propose a Bayesian inference methodology for the analysis and comparison of dose\u2013response experiments.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Our results well demonstrate the informativeness gain of our Bayesian approach in comparison to the commonly used Marquardt\u2013Levenberg algorithm. It is capable to characterize the noise of dataset while inferring probable values distributions for the efficacy metrics. It can also evaluate the difference between the metrics of two datasets and compute the probability that one value is greater than the other. The conclusions that can be drawn from such analyzes are more precise.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>We implemented a simple web interface that allows the users to analyze a single dose\u2013response dataset, as well as to statistically compare the metrics of two datasets.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btz335","type":"journal-article","created":{"date-parts":[[2019,5,10]],"date-time":"2019-05-10T11:32:12Z","timestamp":1557487932000},"page":"i464-i473","source":"Crossref","is-referenced-by-count":12,"title":["Enhancing the drug discovery process: Bayesian inference for the analysis and comparison of dose\u2013response experiments"],"prefix":"10.1093","volume":"35","author":[{"given":"Caroline","family":"Labelle","sequence":"first","affiliation":[{"name":"Institute for Research in Immunology and Cancer, Universit\u00e9 de Montr\u00e9al, Montr\u00e9al, QC, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anne","family":"Marinier","sequence":"additional","affiliation":[{"name":"Institute for Research in Immunology and Cancer, Universit\u00e9 de Montr\u00e9al, Montr\u00e9al, QC, Canada"},{"name":"Department of Chemistry, Faculty of Arts and Science, Universit\u00e9 de Montr\u00e9al, Montr\u00e9al, QC, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"S\u00e9bastien","family":"Lemieux","sequence":"additional","affiliation":[{"name":"Institute for Research in Immunology and Cancer, Universit\u00e9 de Montr\u00e9al, Montr\u00e9al, QC, Canada"},{"name":"Department of Biochemistry, Faculty of Medicine, Universit\u00e9 de Montr\u00e9al, Montr\u00e9al, QC, Canada"},{"name":"Department of Computer Science and Operations Research, Faculty of Arts and Sciences, Universit\u00e9 de Montr\u00e9al, Montr\u00e9al, QC, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2019,7,5]]},"reference":[{"key":"2023062712334589400_btz335-B1","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1214\/12-BA717","article-title":"Combining expert opinions in prior elicitation","volume":"7","author":"Albert","year":"2012","journal-title":"Bayesian Anal"},{"key":"2023062712334589400_btz335-B2","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1088\/0957-0233\/12\/2\/702","article-title":"Bayesian theory","volume":"12","author":"Bernardo","year":"2001","journal-title":"Meas. 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