Robust inference of kinase activity using functional networks

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buir.contributor.authorÇiçek, A. Ercüment
buir.contributor.orcidÇiçek, A. Ercüment|0000-0001-8613-6619
dc.citation.epage12en_US
dc.citation.issueNumber1en_US
dc.citation.spage1en_US
dc.citation.volumeNumber12en_US
dc.contributor.authorYılmaz, S.
dc.contributor.authorAyati, M.
dc.contributor.authorSchlatzer, D.
dc.contributor.authorÇiçek, A. Ercüment
dc.date.accessioned2022-02-07T10:12:56Z
dc.date.available2022-02-07T10:12:56Z
dc.date.issued2021-02-19
dc.departmentDepartment of Computer Engineeringen_US
dc.description.abstractMass spectrometry enables high-throughput screening of phosphoproteins across a broad range of biological contexts. When complemented by computational algorithms, phospho-proteomic data allows the inference of kinase activity, facilitating the identification of dysregulated kinases in various diseases including cancer, Alzheimer’s disease and Parkinson’s disease. To enhance the reliability of kinase activity inference, we present a network-based framework, RoKAI, that integrates various sources of functional information to capture coordinated changes in signaling. Through computational experiments, we show that phosphorylation of sites in the functional neighborhood of a kinase are significantly predictive of its activity. The incorporation of this knowledge in RoKAI consistently enhances the accuracy of kinase activity inference methods while making them more robust to missing annotations and quantifications. This enables the identification of understudied kinases and will likely lead to the development of novel kinase inhibitors for targeted therapy of many diseases. RoKAI is available as web-based tool at http://rokai.io.en_US
dc.identifier.doi10.1038/s41467-021-21211-6en_US
dc.identifier.eissn2041-1723
dc.identifier.urihttp://hdl.handle.net/11693/77108
dc.language.isoEnglishen_US
dc.publisherNature Publishing Groupen_US
dc.relation.isversionofhttps://doi.org/10.1038/s41467-021-21211-6en_US
dc.source.titleNature Communicationsen_US
dc.subjectCellular signalling networksen_US
dc.subjectComputational modelsen_US
dc.subjectData miningen_US
dc.subjectPhosphorylationen_US
dc.subjectSoftwareen_US
dc.titleRobust inference of kinase activity using functional networksen_US
dc.typeArticleen_US
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