One of the primary mechanisms of signal transduction in cells is protein 
phosphorylation. Upon ligand stimulation a series of phosphorylation events take 
place which eventually lead to transcription. Different sets of phosphorylation 
events take place due to different stimulating ligands in different types of 
cells. Knowledge of these phosphorylation events is essential to understand the 
underlying signaling mechanisms. We have developed a Bayesian framework to infer 
phosphorylation networks from time series measurements of phosphosite 
concentrations upon ligand stimulation. To increase the prediction accuracy we 
integrated different types of data, e.g., amino acid sequence data, genomic 
context data (gene fusion, gene neighborhood, and phylogentic profiles), primary 
experimental evidence (physical protein interactions and gene coexpression), 
manually curated pathway databases, and automatic literature mining with time 
series data in our inference framework. We compared our results with data 
available from public databases and report a high level of prediction accuracy.