bioRxiv preprint doi: https://doi.org/10.1101/865055; this version posted December 4, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. Accurate Prediction of Kinase-Substrate Networks Using Knowledge Graphs V´ıtNov´aˇcek1∗+, Gavin McGauran3, David Matallanas3, Adri´anVallejo Blanco3,4, Piero Conca2, Emir Mu~noz1,2, Luca Costabello2, Kamalesh Kanakaraj1, Zeeshan Nawaz1, Sameh K. Mohamed1, Pierre-Yves Vandenbussche2, Colm Ryan3, Walter Kolch3,5,6, Dirk Fey3,6∗ 1Data Science Institute, National University of Ireland Galway, Ireland 2Fujitsu Ireland Ltd., Co. Dublin, Ireland 3Systems Biology Ireland, University College Dublin, Belfield, Dublin 4, Ireland 4Department of Oncology, Universidad de Navarra, Pamplona, Spain 5Conway Institute of Biomolecular & Biomedical Research, University College Dublin, Belfield, Dublin 4, Ireland 6School of Medicine, University College Dublin, Belfield, Dublin 4, Ireland ∗ Corresponding authors (
[email protected],
[email protected]). + Lead author. 1 bioRxiv preprint doi: https://doi.org/10.1101/865055; this version posted December 4, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. Abstract Phosphorylation of specific substrates by protein kinases is a key control mechanism for vital cell-fate decisions and other cellular pro- cesses. However, discovering specific kinase-substrate relationships is time-consuming and often rather serendipitous.