We present a new methodology for computational analysis of gene and protein networks. In the suggested framework, data from diverse sources is utilized to computationally suggest expansion of known pathways. The aim is to generate new educated hypotheses on gene functions and on the logic of the biological network circuitry. The framework enables the utilization of multiple data sources (gene expression profiles, protein interactions etc.) and supports the incorporation of biologically motivated constraints and rules to improve specificity. We have developed a combinatorial probabilistic approach to estimate the quality of suggested network expansion. The approach has been implemented in an interactive software prototype tool, and preliminary results seem promising.
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