DreamAI: algorithm for the imputation of proteomics data
bioRxiv (Cold Spring Harbor Laboratory)
Abstract
Abstract Deep proteomics profiling using labeled LC-MS/MS experiments has been proven to be powerful to study complex diseases. However, due to the dynamic nature of the discovery mass spectrometry, the generated data contain a substantial fraction of missing values. This poses great challenges for data analyses, as many tools, especially those for high dimensional data, cannot deal with missing values directly. To address this problem, the NCI-CPTAC Proteogenomics DREAM Challenge was carried out to develop effective imputation algorithms for labeled LC-MS/MS proteomics data through crowd learning. The final resulting algorithm, DreamAI, is based on an ensemble of six different imputation methods. The imputation accuracy of DreamAI, as measured by Pearson correlation, is about 15%-50% greater than existing tools among less abundant proteins, which are more vulnerable to be missed in proteomics data sets. This new tool notably enhances data analysis capabilities in proteomics research.
Authors 23
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Icahn School of Medicine at Mount Sinai
Affiliation as printed
Icahn School of Medicine at Mount Sinai, USA
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Affiliation as printed
Department of Computer Science and Engineering, Korea University, South Korea
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Icahn School of Medicine at Mount Sinai
Affiliation as printed
Icahn School of Medicine at Mount Sinai, USA
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Affiliation as printed
New York University, USA
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Heidelberg University · Stanford Cancer Institute · RWTH Aachen University · Stanford University
Affiliation as printed
Division of Oncology, Department of Medicine, Stanford Cancer Institute, Stanford University, USA
Faculty of Biosciences, Heidelberg University, Germany
JRC for Computational Biomedicine, RWTH Aachen University, Faculty of Medicine, Aachen, Germany
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Icahn School of Medicine at Mount Sinai
Affiliation as printed
Icahn School of Medicine at Mount Sinai, USA
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Icahn School of Medicine at Mount Sinai
Affiliation as printed
Icahn School of Medicine at Mount Sinai, USA
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Affiliation as printed
University of Colorado, USA
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University of California, Los Angeles
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Department of Statistics, University of California Los Angeles, USA
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University of California, Los Angeles
Affiliation as printed
Department of Statistics, University of California Los Angeles, USA
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Affiliation as printed
Department of Mathematics, Tsinghua University, China
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Affiliation as printed
Sage Bionetworks, USA
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Icahn School of Medicine at Mount Sinai
Affiliation as printed
Icahn School of Medicine at Mount Sinai, USA
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Affiliation as printed
Georgetown University, USA
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Affiliation as printed
Brigham Young University, USA
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University of California, Los Angeles
Affiliation as printed
University of California, Los Angeles, USA
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Affiliation as printed
National Cancer Institute, USA
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Affiliation as printed
IBM Research & Mount Sinai, USA
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Icahn School of Medicine at Mount Sinai
Affiliation as printed
Icahn School of Medicine at Mount Sinai, USA
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Affiliation as printed
Department of Computer Science and Engineering, Korea University, South Korea
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Affiliation as printed
New York University, USA
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Heidelberg University · University Hospital Heidelberg · RWTH Aachen University
Affiliation as printed
Institute for Computational Biomedicine, Heidelberg University, Faculty of Medicine, & Heidelberg University Hospital, Germany
JRC for Computational Biomedicine, RWTH Aachen University, Faculty of Medicine, Aachen, Germany
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Pei Wang corresponding
Icahn School of Medicine at Mount Sinai
Affiliation as printed
Icahn School of Medicine at Mount Sinai, USA
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