New tools for unbiased cell signaling pathway inference from multi-omics data are needed. Here we propose a new unbiased method, the unbiased pathway analysis (UPA), to model and combine omics data into regulatory complexes and pathways. UPA was validated with publicly available omics data and was found accurate in discovering protein-protein interactions, kinase substrate phosphosite relationships, transcription factor target gene relationships, and signaling pathways. Transcription factor, kinase, subcellular location and function prediction algorithms were devised for transcriptome, phosphoproteome and interactome regulatory complexes and pathways, respectively. To apply UPA in a biologically relevant context, interactome, phosphoproteome, transcriptome and proteome data were collected from analyses carried out using melanoma cells to address gamma-secretase cleavage-dependent signaling characteristics of the receptor tyrosine kinase TYRO3. Significant differences were discovered between full-length and gamma-secretase cleaved TYRO3 when signaling modules associated with cell cycle and growth, cell adhesion and motility, cell morphology, or immune responses were compared. The pathways modeled with UPA reflected both the predicted function and direction of the predicted function in validation experiments.