{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T03:32:13Z","timestamp":1762918333252,"version":"3.37.3"},"reference-count":10,"publisher":"Oxford University Press (OUP)","issue":"11","license":[{"start":{"date-parts":[[2022,4,15]],"date-time":"2022-04-15T00:00:00Z","timestamp":1649980800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,5,26]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Motivation<\/jats:title><jats:p>Causal inference on high-dimensional feature data can be used to find a profile of patients who will benefit the most from treatment rather than no treatment. However, there is a need for usable implementations for transcriptomic data. We developed teff that applies random causal forest on gene expression data to target individuals with high expected treatment effects.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We extracted a profile of high benefit of treating psoriasis with brodalumab and observed that it was associated with higher T cell abundance in non-lesional skin at baseline and a lower response for etanercept in an independent study. Individual patient targeting with causal inference profiling can inform patients on choosing between treatments before the intervention begins.<\/jats:p><\/jats:sec><jats:sec><jats:title>Availability and implementation<\/jats:title><jats:p>teff is an R package available at https:\/\/teff-package.github.io. The data underlying this article are available in GEO, at https:\/\/www.ncbi.nlm.nih.gov\/geo\/<\/jats:p><\/jats:sec><jats:sec><jats:title>Supplementary information<\/jats:title><jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p><\/jats:sec>","DOI":"10.1093\/bioinformatics\/btac269","type":"journal-article","created":{"date-parts":[[2022,4,14]],"date-time":"2022-04-14T11:10:15Z","timestamp":1649934615000},"page":"3124-3125","source":"Crossref","is-referenced-by-count":4,"title":["<i>teff<\/i>: estimation of Treatment EFFects on transcriptomic data using causal random forest"],"prefix":"10.1093","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8551-6695","authenticated-orcid":false,"given":"Alejandro","family":"C\u00e1ceres","sequence":"first","affiliation":[{"name":"Instituto de Salud Global de Barcelona (ISGlobal) , 08003 Barcelona, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3267-2146","authenticated-orcid":false,"given":"Juan R","family":"Gonz\u00e1lez","sequence":"additional","affiliation":[{"name":"Instituto de Salud Global de Barcelona (ISGlobal) , 08003 Barcelona, Spain"}]}],"member":"286","published-online":{"date-parts":[[2022,4,15]]},"reference":[{"key":"2023041402570573000_","doi-asserted-by":"crossref","first-page":"1148","DOI":"10.1214\/18-AOS1709","article-title":"Generalized random forests","volume":"47","author":"Athey","year":"2019","journal-title":"Ann. 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