{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T12:01:22Z","timestamp":1777118482786,"version":"3.51.4"},"reference-count":13,"publisher":"Oxford University Press (OUP)","issue":"13","license":[{"start":{"date-parts":[[2018,11,21]],"date-time":"2018-11-21T00:00:00Z","timestamp":1542758400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1452656"],"award-info":[{"award-number":["1452656"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"name":"CAREER: On-line Service for Predicting Protein Phosphorylation Dynamics Under Unseen Perturbations"},{"DOI":"10.13039\/100015711","name":"Michigan Institute for Data Science","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100015711","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100015711","name":"MIDAS","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100015711","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Michigan Center for Single-Cell Genomic Data Analytics"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,7,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Combination therapy is widely used in cancer treatment to overcome drug resistance. High-throughput drug screening is the standard approach to study the drug combination effects, yet it becomes impractical when the number of drugs under consideration is large. Therefore, accurate and fast computational tools for predicting drug synergistic effects are needed to guide experimental design for developing candidate drug pairs.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Here, we present TAIJI, a high-performance software for fast and accurate prediction of drug synergism. It is based on the winning algorithm in the AstraZeneca-Sanger Drug Combination Prediction DREAM Challenge, which is a unique platform to unbiasedly evaluate the performance of current state-of-the-art methods, and includes 160 team-based submission methods. When tested across a broad spectrum of 85 different cancer cell lines and 1089 drug combinations, TAIJI achieved a high prediction correlation (0.53), approaching the accuracy level of experimental replicates (0.56). The runtime is at the scale of minutes to achieve this state-of-the-field performance.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>TAIJI is freely available on GitHub (https:\/\/github.com\/GuanLab\/TAIJI). It is functional with built-in Perl and Python.<\/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\/bty955","type":"journal-article","created":{"date-parts":[[2018,11,20]],"date-time":"2018-11-20T20:12:21Z","timestamp":1542744741000},"page":"2338-2339","source":"Crossref","is-referenced-by-count":16,"title":["TAIJI: approaching experimental replicates-level accuracy for drug synergy prediction"],"prefix":"10.1093","volume":"35","author":[{"given":"Hongyang","family":"Li","sequence":"first","affiliation":[{"name":"Department of Computational Medicine and Bioinformatics"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuai","family":"Hu","sequence":"additional","affiliation":[{"name":"Department of Computational Medicine and Bioinformatics"},{"name":"Department of Medicinal Chemistry, College of Pharmacy, Rogel Cancer Center and"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nouri","family":"Neamati","sequence":"additional","affiliation":[{"name":"Department of Medicinal Chemistry, College of Pharmacy, Rogel Cancer Center and"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanfang","family":"Guan","sequence":"additional","affiliation":[{"name":"Department of Computational Medicine and Bioinformatics"},{"name":"Nephrology Division, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2018,11,21]]},"reference":[{"key":"2023051701213476200_bty955-B1","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1038\/nbt.2284","article-title":"Combinatorial drug therapy for cancer in the post-genomic era","volume":"30","author":"Al-Lazikani","year":"2012","journal-title":"Nat. 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