DreamAI: algorithm for the imputation of proteomics data
By
weiping ma,
Sunkyu Kim,
SHRABANTI CHOWDHURY,
zhi li,
Mi YANG,
Seungyeul Yoo,
Francesca Petralia,
Jeremy Jacobsen,
Jingyi Jessica Li,
Xinzhou J Ge,
Kexin Li,
Thomas V Yu,
Anna Calinawan,
Nathan John Edwards,
Samuel H Payne,
Paul C Boutros,
Henry Rodriguez,
Gustavo Stolovitzky,
Jaewoo Kang,
David Fenyo,
Julio Saez-Rodriguez,
Pei Wang
Posted 22 Jul 2020
bioRxiv DOI: 10.1101/2020.07.21.214205
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.
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