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@article{1754803, author = {Nováček, Vít and McGauran, Gavin and Matallanas, David and Blanco, Adrián Vallejo and Conca, Piero and Muñoz, Emir and Costabello, Luca and Kanakaraj, Kamalesh and Nawaz, Zeeshan and Walsh, Brian and Mohamed, Sameh K and Vandenbussche, PierreandYves and Ryan, Colm J and Kolch, Walter and Fey, Dirk}, article_location = {Cambridge}, article_number = {12}, doi = {http://dx.doi.org/10.1371/journal.pcbi.1007578}, keywords = {knowledge graphs; kinase-substrate networks; phosphorylation prediction; relational machine learning}, language = {eng}, issn = {1553-734X}, journal = {PLoS Computational Biology}, title = {Accurate prediction of kinase-substrate networks using knowledge graphs}, url = {https://figshare.com/projects/Accurate_Prediction_of_Kinase-Substrate_Networks_Using_Knowledge_Graphs/79254}, volume = {16}, year = {2020} }
TY - JOUR ID - 1754803 AU - Nováček, Vít - McGauran, Gavin - Matallanas, David - Blanco, Adrián Vallejo - Conca, Piero - Muñoz, Emir - Costabello, Luca - Kanakaraj, Kamalesh - Nawaz, Zeeshan - Walsh, Brian - Mohamed, Sameh K - Vandenbussche, Pierre-Yves - Ryan, Colm J - Kolch, Walter - Fey, Dirk PY - 2020 TI - Accurate prediction of kinase-substrate networks using knowledge graphs JF - PLoS Computational Biology VL - 16 IS - 12 SP - 1-30 EP - 1-30 PB - PLoS SN - 1553734X KW - knowledge graphs KW - kinase-substrate networks KW - phosphorylation prediction KW - relational machine learning UR - https://figshare.com/projects/Accurate_Prediction_of_Kinase-Substrate_Networks_Using_Knowledge_Graphs/79254 N2 - Phosphorylation of specific substrates by protein kinases is a key control mechanism for vital cell-fate decisions and other cellular processes. However, discovering specific kinase-substrate relationships is time-consuming and often rather serendipitous. Computational predictions alleviate these challenges, but the current approaches suffer from limitations like restricted kinome coverage and inaccuracy. They also typically utilise only local features without reflecting broader interaction context. To address these limitations, we have developed an alternative predictive model. It uses statistical relational learning on top of phosphorylation networks interpreted as knowledge graphs, a simple yet robust model for representing networked knowledge. Compared to a representative selection of six existing systems, our model has the highest kinome coverage and produces biologically valid high-confidence predictions not possible with the other tools. Specifically, we have experimentally validated predictions of previously unknown phosphorylations by the LATS1, AKT1, PKA and MST2 kinases in human. Thus, our tool is useful for focusing phosphoproteomic experiments, and facilitates the discovery of new phosphorylation reactions. Our model can be accessed publicly via an easy-to-use web interface (LinkPhinder). ER -
NOVÁČEK, Vít, Gavin MCGAURAN, David MATALLANAS, Adrián Vallejo BLANCO, Piero CONCA, Emir MUÑOZ, Luca COSTABELLO, Kamalesh KANAKARAJ, Zeeshan NAWAZ, Brian WALSH, Sameh K MOHAMED, Pierre-Yves VANDENBUSSCHE, Colm J RYAN, Walter KOLCH and Dirk FEY. Accurate prediction of kinase-substrate networks using knowledge graphs. \textit{PLoS Computational Biology}. Cambridge: PLoS, 2020, vol.~16, No~12, p.~1-30. ISSN~1553-734X. Available from: https://dx.doi.org/10.1371/journal.pcbi.1007578.
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