Positive-unlabeled ensemble learning for kinase substrate prediction from dynamic phosphoproteomics data Article Swipe
Pengyi Yang
,
Sean J. Humphrey
,
David E. James
,
Jean Yang
,
Raja Jothi
·
YOU?
·
· 2015
· Open Access
·
· DOI: https://doi.org/10.1093/bioinformatics/btv550
YOU?
·
· 2015
· Open Access
·
· DOI: https://doi.org/10.1093/bioinformatics/btv550
Motivation: Protein phosphorylation is a post-translational modification that underlines various aspects of cellular signaling. A key step to reconstructing signaling networks involves identification of the set of all kinases and their substrates. Experimental characterization of kinase substrates is both expensive and time-consuming. To expedite the discovery of novel substrates, computational approaches based on kinase recognition sequence (motifs) from known substrates, protein structure, interaction and co-localization have been proposed. However, rarely do these methods take into account the dynamic responses of signaling cascades measured from in vivo cellular systems. Given that recent advances in mass spectrometry-based technologies make it possible to quantify phosphorylation on a proteome-wide scale, computational approaches that can integrate static features with dynamic phosphoproteome data would greatly facilitate the prediction of biologically relevant kinase-specific substrates. Results: Here, we propose a positive-unlabeled ensemble learning approach that integrates dynamic phosphoproteomics data with static kinase recognition motifs to predict novel substrates for kinases of interest. We extended a positive-unlabeled learning technique for an ensemble model, which significantly improves prediction sensitivity on novel substrates of kinases while retaining high specificity. We evaluated the performance of the proposed model using simulation studies and subsequently applied it to predict novel substrates of key kinases relevant to insulin signaling. Our analyses show that static sequence motifs and dynamic phosphoproteomics data are complementary and that the proposed integrated model performs better than methods relying only on static information for accurate prediction of kinase-specific substrates. Availability and implementation: Executable GUI tool, source code and documentation are freely available at https://github.com/PengyiYang/KSP-PUEL. Contact: [email protected] or [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.
Related Topics
Concepts
Phosphoproteomics
Computer science
Computational biology
Kinase
Proteome
Executable
Artificial intelligence
Machine learning
Protein phosphorylation
Bioinformatics
Biology
Protein kinase A
Biochemistry
Operating system
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/bioinformatics/btv550
- https://academic.oup.com/bioinformatics/article-pdf/32/2/252/49016333/bioinformatics_32_2_252.pdf
- OA Status
- bronze
- Cited By
- 42
- References
- 42
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2248525918
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W2248525918Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1093/bioinformatics/btv550Digital Object Identifier
- Title
-
Positive-unlabeled ensemble learning for kinase substrate prediction from dynamic phosphoproteomics dataWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2015Year of publication
- Publication date
-
2015-09-22Full publication date if available
- Authors
-
Pengyi Yang, Sean J. Humphrey, David E. James, Jean Yang, Raja JothiList of authors in order
- Landing page
-
https://doi.org/10.1093/bioinformatics/btv550Publisher landing page
- PDF URL
-
https://academic.oup.com/bioinformatics/article-pdf/32/2/252/49016333/bioinformatics_32_2_252.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
bronzeOpen access status per OpenAlex
- OA URL
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https://academic.oup.com/bioinformatics/article-pdf/32/2/252/49016333/bioinformatics_32_2_252.pdfDirect OA link when available
- Concepts
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Phosphoproteomics, Computer science, Computational biology, Kinase, Proteome, Executable, Artificial intelligence, Machine learning, Protein phosphorylation, Bioinformatics, Biology, Protein kinase A, Biochemistry, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
42Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 2, 2023: 3, 2022: 4, 2021: 5, 2020: 5Per-year citation counts (last 5 years)
- References (count)
-
42Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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