دورية أكاديمية
A Crowdsourcing Approach to Developing and Assessing Prediction Algorithms for AML Prognosis
| العنوان: | A Crowdsourcing Approach to Developing and Assessing Prediction Algorithms for AML Prognosis |
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| المؤلفون: | Noren 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, Abrams Zachary, Ambrosini Giovanna, Anastassiou Dimitris, Baladandayuthapani Veerabhadran, Batten Kimberly, Bucher Philipp, Buturovic Ljubomir, Campion Loic, Creighton Chad J., Chen Greg, Cheong Jae-Ho, Barbara Di Camillo, Dreos René, Estrada Alan, Fatemi Seyyed A., Fitzgerald Andrew, Flynn Jennifer, Fronczuk Maciej, Gu Weiyi, Guha Subharup, Hosseini Maryam, Hung Ling-Hong, Hunter Geoffrey, Hwang Tae Hyun, Kim Daniel, Kim Minsoo, Korra Jyothi, Krstajic Damjan, Kumar Sunil, Kuh Anthony, Li Jinpu, Liu Yashu, Mcmurray James, Morgan Daniel, Motiwala Tasneem, Naegle Kristen, Niemiec Rafał, Oehler Vivian G., Park Sunho, Pattin Alejandrina, Peabody Andrea, Piraino Scott W., Regan Kelly, Ronan Tom, Rościszewski Antoni, Rudnicki Witold, Sanavia Tiziana, Santhanam Narayana, Shay Jerry, Tang Hao, Vilar Jose M. G., Wang Tao, Wright Woodring, Wrzesień Mariusz, Xiao Guanghua, Xie Yang, Yang Sen, Yang Tai-Hsien Ou, Yang Tao, Ye Jieping, Yeung Ka Yee, Zang Xiao, Zolfaghar Kiyana, Żuk Paweł, Norman Thea, Friend Stephen H., Stolovitzky Gustavo, Kornblau Steven, Qutub Amina A. |
| المساهمون: | Noren 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, Abrams Zachary, Ambrosini Giovanna, Anastassiou Dimitri, Baladandayuthapani Veerabhadran, Batten Kimberly, Bucher Philipp, Buturovic Ljubomir, Campion Loic, Creighton Chad J., Chen Greg, Cheong Jae-Ho, Barbara Di Camillo, Dreos René, Estrada Alan, Fatemi Seyyed A., Fitzgerald Andrew, Flynn Jennifer, Fronczuk Maciej, Gu Weiyi, Guha Subharup, Hosseini Maryam, Hung Ling-Hong, Hunter Geoffrey, Hwang Tae Hyun, Kim Daniel, Kim Minsoo, Korra Jyothi, Krstajic Damjan, Kumar Sunil, Kuh Anthony, Li Jinpu, Liu Yashu, Mcmurray Jame, Morgan Daniel, Motiwala Tasneem, Naegle Kristen, Niemiec Rafał, Oehler Vivian G., Park Sunho, Pattin Alejandrina, Peabody Andrea, Piraino Scott W., Regan Kelly, Ronan Tom, Rościszewski Antoni, Rudnicki Witold, Sanavia Tiziana, Santhanam Narayana, Shay Jerry, Tang Hao, Vilar Jose M. G., Wang Tao, Wright Woodring, Wrzesień Mariusz, Xiao Guanghua, Xie Yang, Yang Sen, Yang Tai-Hsien Ou, Yang Tao, Ye Jieping, Yeung Ka Yee, Zang Xiao, Zolfaghar Kiyana, Żuk Paweł, Norman Thea, Friend Stephen H., Stolovitzky Gustavo, Kornblau Steven, Qutub Amina A. |
| سنة النشر: | 2016 |
| المجموعة: | Università degli studi di Torino: AperTo (Archivio Istituzionale ad Accesso Aperto) |
| مصطلحات موضوعية: | Acute Myeloid Leukemia, Prediction algorithms, Machine Learning, Bioinformatics |
| الوصف: | 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. |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| العلاقة: | info:eu-repo/semantics/altIdentifier/pmid/27351836; info:eu-repo/semantics/altIdentifier/wos/WOS:000379349700013; volume:12; issue:6; firstpage:1; lastpage:16; numberofpages:16; journal:PLOS COMPUTATIONAL BIOLOGY; http://hdl.handle.net/2318/1727735Test; info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-84978888838; https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1004890&type=printableTest |
| DOI: | 10.1371/journal.pcbi.1004890 |
| DOI: | 10.1371/journal.pcbi.1004890&type=printable |
| الإتاحة: | https://doi.org/10.1371/journal.pcbi.1004890Test http://hdl.handle.net/2318/1727735Test https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1004890&type=printableTest |
| حقوق: | info:eu-repo/semantics/openAccess |
| رقم الانضمام: | edsbas.2CBC963A |
| قاعدة البيانات: | BASE |
| DOI: | 10.1371/journal.pcbi.1004890 |
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