ArticlePLoS computational biology2016
A Crowdsourcing Approach to Developing and Assessing Prediction Algorithms for AML Prognosis.
Article in PLoS computational biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Crowd-sourced benchmarking of single-sample tumor subclonal reconstruction.Nature biotechnology · 2025Article
- Molecular Insight into Iron Homeostasis of Acute Myeloid Leukemia Blasts.International journal of molecular sciences · 2023Article
- Prediction of complete remission and survival in acute myeloid leukemia using supervised machine learning.Haematologica · 2023Article
- Model guided trait-specific co-expression network estimation as a new perspective for identifying molecular interactions and pathways.PLoS computational biology · 2021Article
- Estimating Linear and Nonlinear Gene Coexpression Networks by Semiparametric Neighborhood Selection.Genetics · 2020Article
- Scalable Nonparametric Prescreening Method for Searching Higher-Order Genetic Interactions Underlying Quantitative Traits.Genetics · 2019Article
- Development and validation of GMI signature based random survival forest prognosis model to predict clinical outcome in acute myeloid leukemia.BMC medical genomics · 2019Article
- Machine learning and feature selection for drug response prediction in precision oncology applications.Biophysical reviews · 2019Review
- Stratification of amyotrophic lateral sclerosis patients: a crowdsourcing approach.Scientific reports · 2019Article
- Article
- Proteomics Versus Clinical Data and Stochastic Local Search Based Feature Selection for Acute Myeloid Leukemia Patients' Classification.Journal of medical systems · 2018Article
- Global proteomics profiling improves drug sensitivity prediction: results from a multi-omics, pan-cancer modeling approach.Bioinformatics (Oxford, England) · 2018Article
- Discriminate the response of Acute Myeloid Leukemia patients to treatment by using proteomics data and Answer Set Programming.BMC bioinformatics · 2018Article
- A community approach to mortality prediction in sepsis via gene expression analysis.Nature communications · 2018Article
- A machine learning classifier trained on cancer transcriptomes detects NF1 inactivation signal in glioblastoma.BMC genomics · 2017Article
- Computational dynamic approaches for temporal omics data with applications to systems medicine.BioData mining · 2017Review
- Evolution-informed modeling improves outcome prediction for cancers.Evolutionary applications · 2017Article
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22 authors.
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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.
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