%0 Journal Article %A Borgthor Petursson %A Evangelia Petsalaki %T Data-driven extraction of human kinase-substrate relationships from omics datasets %D 2022 %R 10.1101/2022.01.15.476449 %J bioRxiv %P 2022.01.15.476449 %X Phosphorylation forms an important part of the signalling system that cells use for decision making and regulation of processes such as celll division and differentiation. To date, a large portion of identified phosphosites are not known to be targeted by any kinase. At the same time around 30% of kinases have no known target. This knowledge gap stresses the need to make large scale, data-driven computational predictions. In this paper, we have created a machine learning-based model to derive a probabilistic kinase-substrate network from omics datasets. We show that our methodology displays improved performance compared to other state of the art kinase-substrate predictions, and provides predictions for more kinases than most of them. Importantly, it better captures new experimentally-identified kinase-substrate relationships. It can therefore allow the improved prioritisation of kinase-substrate pairs for illuminating the dark human cell signalling space.Competing Interest StatementThe authors have declared no competing interest. %U https://www.biorxiv.org/content/biorxiv/early/2022/01/18/2022.01.15.476449.full.pdf