Amidst the proteomes of human tissues lie subsets of proteins that are closely involved in conserved pathophysiological processes. Much of biomedical research concerns interrogating disease signature proteins and defining their roles in disease mechanisms. With advances in proteomics technologies, it is now feasible to develop targeted proteomics assays that can accurately quantify protein abundance as well as their post-translational modifications; however, with rapidly accumulating number of studies implicating proteins in diseases, current resources are insufficient to target every protein without judiciously prioritizing the proteins with high significance and impact for assay development. We describe here a data science method to prioritize and expedite assay development on high-impact proteins across research fields by leveraging the biomedical literature record to rank and normalize proteins that are popularly and preferentially published by biomedical researchers. We demonstrate this method by finding priority proteins across six major physiological systems (cardiovascular, cerebral, hepatic, renal, pulmonary, and intestinal). The described method is data-driven and builds upon the collective knowledge of previous publications referenced on PubMed to lend objectivity to target selection. The method and resulting popular protein lists may also be useful for exploring biological processes associated with various physiological systems and research topics, in addition to benefiting ongoing efforts to facilitate the broad translation of proteomics technologies.
Keywords: bibliometrics; common proteins; data science; human tissue convergence; proteomics translation; semantics; targeted proteomics.