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dc.contributor.authorNoren, David P.
Long, Byron L.
Norel, Raquel
Rrhissorrakrai, Kahn
Hess, Kenneth
Hu, Chenyue Wendy
Bisberg, Alex J.
Schultz, Andre
Engquist, Erik
Liu, Li
Lin, Xihui
Chen, Gregory M.
Xie, Honglei
Hunter, Geoffrey A.M.
Boutros, Paul C.
Stepanov, Oleg
DREAM 9 AML-OPC Consortium
Norman, Thea
Friend, Stephen H.
Stolovitzky, Gustavo
Kornblau, Steven
Qutub, Amina A.
dc.date.accessioned 2016-09-30T20:52:23Z
dc.date.available 2016-09-30T20:52:23Z
dc.date.issued 2016
dc.identifier.citation Noren, David P., Long, Byron L., Norel, Raquel, et al.. "A Crowdsourcing Approach to Developing and Assessing Prediction Algorithms for AML Prognosis." PLoS Computational Biology, 12, no. 6 (2016) Public Library of Science: http://dx.doi.org/10.1371/journal.pcbi.1004890.
dc.identifier.urihttps://hdl.handle.net/1911/91645
dc.description.abstract Acute Myeloid Leukemia (AML) is a fatal hematological cancer. The genetic abnormalities underlying AML are extremely heterogeneous among patients, making prognosis and treatment selection very difficult. While clinical proteomics data has the potential to improve prognosis accuracy, thus far, the quantitative means to do so have yet to be developed. Here we report the results and insights gained from the DREAM 9 Acute Myeloid Prediction Outcome Prediction Challenge (AML-OPC), a crowdsourcing effort designed to promote the development of quantitative methods for AML prognosis prediction. We identify the most accurate and robust models in predicting patient response to therapy, remission duration, and overall survival. We further investigate patient response to therapy, a clinically actionable prediction, and find that patients that are classified as resistant to therapy are harder to predict than responsive patients across the 31 models submitted to the challenge. The top two performing models, which held a high sensitivity to these patients, substantially utilized the proteomics data to make predictions. Using these models, we also identify which signaling proteins were useful in predicting patient therapeutic response.
dc.language.iso eng
dc.publisher Public Library of Science
dc.rights This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.title A Crowdsourcing Approach to Developing and Assessing Prediction Algorithms for AML Prognosis
dc.type Journal article
dc.citation.journalTitle PLoS Computational Biology
dc.citation.volumeNumber 12
dc.citation.issueNumber 6
dc.type.dcmi Text
dc.identifier.doihttp://dx.doi.org/10.1371/journal.pcbi.1004890
dc.type.publication publisher version
dc.citation.articleNumber e1004890


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This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Except where otherwise noted, this item's license is described as This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.