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Bioinformatics. 2019 Aug 13. pii: btz639. doi: 10.1093/bioinformatics/btz639. [Epub ahead of print]

Machine Learning Empowers Phosphoproteome Prediction in Cancers.

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Department of Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, MI 48109, USA.



Reversible protein phosphorylation is an essential post-translational modification regulating protein functions and signaling pathways in many cellular processes. Aberrant activation of signaling pathways often contributes to cancer development and progression. The mass spectrometry-based phosphoproteomics technique is a powerful tool to investigate the site-level phosphorylation of the proteome in a global fashion, paving the way for understanding the regulatory mechanisms underlying cancers. However, this approach is time-consuming and requires expensive instruments, specialized expertise, and a large amount of starting material. An alternative in silico approach is predicting the phosphoproteomic profiles of cancer patients from the available proteomic, transcriptomic, and genomic data.


Here, we present a winning algorithm in the 2017 NCI-CPTAC DREAM Proteogenomics Challenge for predicting phosphorylation levels of the proteome across cancer patients. We integrate four components into our algorithm, including (1) baseline correlations between protein and phosphoprotein abundances, (2) universal protein-protein interactions, (3) shareable regulatory information across cancer tissues, and (4) associations among multi-phosphorylation sites of the same protein. When tested on a large held-out testing dataset of 108 breast and 62 ovarian cancer samples, our method ranked first in both cancer tissues, demonstrating its robustness and generalization ability.


Our code and reproducible results are freely available on GitHub:


Supplementary data are available at Bioinformatics online.

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