{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T06:20:03Z","timestamp":1769840403843,"version":"3.49.0"},"reference-count":18,"publisher":"Oxford University Press (OUP)","issue":"Supplement_1","license":[{"start":{"date-parts":[[2021,7,12]],"date-time":"2021-07-12T00:00:00Z","timestamp":1626048000000},"content-version":"vor","delay-in-days":11,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["T32 EB009403"],"award-info":[{"award-number":["T32 EB009403"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"name":"HHMI-NIBIB Interfaces Initiative"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,8,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>The recent emergence of cloud laboratories\u2014collections of automated wet-lab instruments that are accessed remotely, presents new opportunities to apply Artificial Intelligence and Machine Learning in scientific research. Among these is the challenge of automating the process of optimizing experimental protocols to maximize data quality.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We introduce a new deterministic algorithm, called PaRallel OptimizaTiOn for ClOud Laboratories (PROTOCOL), that improves experimental protocols via asynchronous, parallel Bayesian optimization. The algorithm achieves exponential convergence with respect to simple regret. We demonstrate PROTOCOL in both simulated and real-world cloud labs. In the simulated lab, it outperforms alternative approaches to Bayesian optimization in terms of its ability to find optimal configurations, and the number of experiments required to find the optimum. In the real-world lab, the algorithm makes progress toward the optimal setting.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Data availability and implementation<\/jats:title>\n                  <jats:p>PROTOCOL is available as both a stand-alone Python library, and as part of a R Shiny application at https:\/\/github.com\/clangmead\/PROTOCOL. Data are available at the same repository.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btab291","type":"journal-article","created":{"date-parts":[[2021,4,26]],"date-time":"2021-04-26T20:46:37Z","timestamp":1619469997000},"page":"i451-i459","source":"Crossref","is-referenced-by-count":13,"title":["Asynchronous parallel Bayesian optimization for AI-driven cloud laboratories"],"prefix":"10.1093","volume":"37","author":[{"given":"Trevor S","family":"Frisby","sequence":"first","affiliation":[{"name":"Computational Biology Department, School of Computer Science, Carnegie Mellon University , Pittsburgh, PA 15213, USA"}]},{"given":"Zhiyun","family":"Gong","sequence":"additional","affiliation":[{"name":"Computational Biology Department, School of Computer Science, Carnegie Mellon University , Pittsburgh, PA 15213, USA"}]},{"given":"Christopher James","family":"Langmead","sequence":"additional","affiliation":[{"name":"Computational Biology Department, School of Computer Science, Carnegie Mellon University , Pittsburgh, PA 15213, USA"}]}],"member":"286","published-online":{"date-parts":[[2021,7,12]]},"reference":[{"key":"2023062410284308300_btab291-B15","first-page":"25","volume-title":"Proc. 25th Annual Conference on Learning Theory, Volume 23 of Proceedings of Machine Learning Research, pp. 39.1\u201339.26","author":"Agrawal","year":"2012"},{"key":"2023062410284308300_btab291-B1","first-page":"2546","volume-title":"Advances in Neural Information Processing Systems","author":"Bergstra","year":"2011"},{"key":"2023062410284308300_btab291-B2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1561\/2200000024","article-title":"Regret analysis of stochastic and nonstochastic multi-armed bandit problems","volume":"5","author":"Bubeck","year":"2012","journal-title":"Found. 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