{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T09:54:45Z","timestamp":1768470885297,"version":"3.49.0"},"reference-count":42,"publisher":"Oxford University Press (OUP)","issue":"Supplement_1","license":[{"start":{"date-parts":[[2024,6,28]],"date-time":"2024-06-28T00:00:00Z","timestamp":1719532800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["2134999"],"award-info":[{"award-number":["2134999"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["1U54AG075931"],"award-info":[{"award-number":["1U54AG075931"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["1U24CA268108"],"award-info":[{"award-number":["1U24CA268108"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,6,28]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Analysis of time series transcriptomics data from clinical trials is challenging. Such studies usually profile very few time points from several individuals with varying response patterns and dynamics. Current methods for these datasets are mainly based on linear, global orderings using visit times which do not account for the varying response rates and subgroups within a patient cohort.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We developed a new method that utilizes multi-commodity flow algorithms for trajectory inference in large scale clinical studies. Recovered trajectories satisfy individual-based timing restrictions while integrating data from multiple patients. Testing the method on multiple drug datasets demonstrated an improved performance compared to prior approaches suggested for this task, while identifying novel disease subtypes that correspond to heterogeneous patient response patterns.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The source code and instructions to download the data have been deposited on GitHub at https:\/\/github.com\/euxhenh\/Truffle.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btae241","type":"journal-article","created":{"date-parts":[[2024,6,28]],"date-time":"2024-06-28T09:25:25Z","timestamp":1719566725000},"page":"i151-i159","source":"Crossref","is-referenced-by-count":3,"title":["Integrating patients in time series clinical transcriptomics data"],"prefix":"10.1093","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6940-8683","authenticated-orcid":false,"given":"Euxhen","family":"Hasanaj","sequence":"first","affiliation":[{"name":"Machine Learning Department, Carnegie Mellon University , Pittsburgh, PA 15213, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-9899-0458","authenticated-orcid":false,"given":"Sachin","family":"Mathur","sequence":"additional","affiliation":[{"name":"R&D Data and Computational Sciences, Sanofi , Cambridge, MA 02141, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3430-6051","authenticated-orcid":false,"given":"Ziv","family":"Bar-Joseph","sequence":"additional","affiliation":[{"name":"Machine Learning Department, Carnegie Mellon University , Pittsburgh, PA 15213, United States"},{"name":"R&D Data and Computational Sciences, Sanofi , Cambridge, MA 02141, United States"},{"name":"Computational Biology Department, Carnegie Mellon University , Pittsburgh, PA 15213, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2024,6,28]]},"reference":[{"key":"2024071814115817500_btae241-B1","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1007\/s10142-003-0090-x","article-title":"Gene arrays and temporal patterns of drug response: corticosteroid effects on rat liver","volume":"3","author":"Almon","year":"2003","journal-title":"Funct Integr Genomics"},{"key":"2024071814115817500_btae241-B2","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1038\/75556","article-title":"Gene ontology: tool for the unification of biology. 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