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Despite technological advances in proteomics, incomplete coverage and inconsistency issues persist, resulting in "data holes". These data holes cause the missing protein problem (MPP), where relevant proteins are persistently unobserved, or sporadically observed across samples, hindering biomarker discovery and proper functional characterization. Network-based approaches can provide powerful solutions for resolving these issues. Functional Class Scoring (FCS) is one such method that uses protein complex information to recover missing proteins with weak support. However, FCS has not been evaluated on more recent proteomic technologies with higher coverage, and there is no clear way to evaluate its performance. To address these issues, we devised a more rigorous evaluation schema based on cross-verification between technical replicates and evaluated its performance on data acquired under recent Data-Independent Acquisition (DIA) technologies (viz. SWATH). Although cross-replicate examination reveals some inconsistencies amongst same-class samples, tissue-differentiating signal is nonetheless strongly conserved, confirming that FCS selects for biologically meaningful networks. We also report that predicted missing proteins are statistically significant based on FCS p values. Despite limited cross-replicate verification rates, the predicted missing proteins as a whole have higher peptide support than non-predicted proteins. FCS also predicts missing proteins that are often lost due to weak specific peptide support., (© 2022. The Author(s).)

Networks can resolve many analytical problems in proteomics, including incomplete coverage and inconsistency. Despite high expectations, network-related research in proteomics has experienced only modest growth. In practice, most current research examines non-quantitative usages, for example determining physical interactions among proteins or contextualizing a differential protein list, rather than addressing practical quantitative usages, for example predicting missing proteins or making sample-class predictions. Moreover, many applications are irreproducible and are not widely adopted owing to a lack of common standards, particularly evaluation criteria and gold-standard datasets. A concerted drive towards quantitative applications and convergence towards common standards is essential for 'network-based proteomics' to realize its development potential and make meaningful contributions to clinical applications., (Copyright © 2016 Elsevier Ltd. All rights reserved.)