RIC: Ranking with Interaction Chains and Its Application in Computational Clinical Proteomics Studies

In clinical proteomics, a major goal is to identify important proteins in proteomics data that is based on biological samples. Using biological connections such as protein interactions that link directly or indirectly proteins from earlier studies with those in current proteomics studies, we propose a new algorithm called ranking with interaction chains (RIC) to rank protein biomarker candidates. With RIC, we use statistical measures to set the best parameter values and applied it to proteomics-based breast cancer biomarker studies. Such ranking strategy may also be generalized where a functional priority score for each biomolecule can be computed from different Omics data types.