Machine learning in genomics plays a key role in leveraging high-throughput data, but assessing the
generalizability of performance has been a persistent challenge. Here, we propose to evaluate the
generalizability of gene characterizations through the shape of performance curves. We identify
Functional Equivalence Classes (FECs), uniform subsets of annotated and unannotated genes that
jointly drive performance, by assessing the presence of straight lines in ROC curves. FECs are
widespread across modalities and methods, and can be used to evaluate the extent and contextspecificity of functional annotations in a data-driven manner. For example, FECs suggest that B cell
markers can be decomposed into shared primary markers (10 to 50 genes), and tissue-specific
secondary markers (100 to 500genes). In addition, FECs are compatible with a wide range of
functional encodings, with marker sets spanning at most 5% of the genome and data-driven
extensions of Gene Ontology sets spanning up to 40% of the genome. Simple to assess visually and
statistically, the identification of FECs in performance curves paves the way for novel functional
characterization and increased robustness in analysi