Kong, Weijia, et al. “PROTREC: A probability-based approach for recovering missing proteins based on biological networks.” Journal of Proteomics 250 (2022): 104392. https://doi.org/10.1016/j.jprot.2021.104392
A novel network-based approach for predicting missing proteins (MPs) is proposed here. This approach, PROTREC (short for PROtein RECovery), dominates existing network-based methods – such as Functional Class Scoring (FCS), Hypergeometric Enrichment (HE), and Gene Set Enrichment Analysis (GSEA) – across a variety of proteomics datasets derived from different proteomics data acquisition paradigms: Higher PROTREC scores are much more closely correlated with higher recovery rates of MPs across sample replicates. The PROTREC score, unlike methods reporting p-values, can be directly interpreted as the probability that an unreported protein in a proteomic screen is actually present in the sample being screened.