{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,22]],"date-time":"2025-02-22T00:45:32Z","timestamp":1740185132159,"version":"3.37.3"},"reference-count":17,"publisher":"Oxford University Press (OUP)","issue":"17","license":[{"start":{"date-parts":[[2022,7,8]],"date-time":"2022-07-08T00:00:00Z","timestamp":1657238400000},"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\/100004917","name":"Cancer Prevention and Research Institute of Texas","doi-asserted-by":"publisher","award":["RR190079"],"award-info":[{"award-number":["RR190079"]}],"id":[{"id":"10.13039\/100004917","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Recruitment of Established Investigators"},{"DOI":"10.13039\/100007313","name":"University of Texas MD Anderson Cancer Center","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100007313","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","award":["R03CA270725"],"award-info":[{"award-number":["R03CA270725"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,9,2]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Summary<\/jats:title><jats:p>In the era of big data, machine learning techniques are widely applied to every area in biomedical research including survival analysis. It is well recognized that censoring, which is a common missing issue in survival time data, hampers the direct usage of these machine learning techniques. Here, we present CondiS, a web toolkit with graphical user interface to help impute the survival times for censored observations and predict the survival times for future enrolled patients. CondiS imputes a censored survival time based on its distribution conditional on its observed part. When covariates are available, CondiS-X incorporates this information to further increase the imputation accuracy. Users can also upload data of newly enrolled patients and predict their survival times. As the first web-app tool with an imputation function for censored lifetime data, CondiS web can facilitate conducting survival analysis with machine learning approaches.<\/jats:p><\/jats:sec><jats:sec><jats:title>Availability and implementation<\/jats:title><jats:p>CondiS is an open-source application implemented with Shiny in R, available free at: https:\/\/biostatistics.mdanderson.org\/shinyapps\/CondiS\/.<\/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\/btac461","type":"journal-article","created":{"date-parts":[[2022,7,8]],"date-time":"2022-07-08T13:21:00Z","timestamp":1657286460000},"page":"4252-4254","source":"Crossref","is-referenced-by-count":1,"title":["CondiS web app: imputation of censored lifetimes for machine learning-based survival analysis"],"prefix":"10.1093","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1870-0019","authenticated-orcid":false,"given":"Yizhuo","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Biostatistics, The University of Texas MD Anderson Cancer Center , Houston, TX 77030, USA"}]},{"given":"Christopher R","family":"Flowers","sequence":"additional","affiliation":[{"name":"Department of Lymphoma\/Myeloma, The University of Texas MD Anderson Cancer Center , Houston, TX 77030, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8359-0533","authenticated-orcid":false,"given":"Ziyi","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, The University of Texas MD Anderson Cancer Center , Houston, TX 77030, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1192-9336","authenticated-orcid":false,"given":"Xuelin","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, The University of Texas MD Anderson Cancer Center , Houston, TX 77030, USA"}]}],"member":"286","published-online":{"date-parts":[[2022,7,8]]},"reference":[{"key":"2023041408371120800_","first-page":"821","article-title":"Theoretical foundations of the potential function method in pattern recognition learning","volume":"25","author":"Aizerman","year":"1964","journal-title":"Autom. 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