Schematic representation of the methodology. The Ontology Fingerprints of the whole human genome were constructed, followed by calculating gene-gene similarity scores using pair-wise comparison of their Ontology Fingerprints. When searching for a cell-type-specific network, the canonical signaling network was repeatedly and stochastically modified by adding or deleting edges based on similarity scores, i.e. the higher the similarity score of a gene pair, the greater possibility of adding the edges connecting the two genes. The candidate networks were trained in parallel using an MCEM (MCMC sampling-based EM) algorithm to infer the states of hidden nodes and estimate network parameters, and LASSO regression was applied in the last round of MCEM. A model selection criteria (BIC) is further calculated for each candidate network. Finally, the best network was selected under the guidance of BIC criteria. The selected network was then applied to predict the phosphorylation activities for the testing data.