Over-representation analysis (ORA) is widely used to interpret selected-gene lists, including differentially expressed genes and cell-type marker genes. Unlike ranked-list gene-set enrichment analysis (GSEA), ORA tests whether selected genes are over-represented in predefined gene sets under an explicit background. Repeated analysis of large gene-set libraries, however, can be computationally costly. GPU-accelerated methods such as rapidGSEA mainly target ranked-list GSEA and do not address repeated exact-ORA processing. We developed paraORA to accelerate repeated ORA by preparing a gene-set library once and reusing its compressed representation across queries. In the GPU-exact path, overlap counting, exact hypergeometric testing, Benjamini–Hochberg correction, and odds-ratio calculation are performed on the GPU. Across 425 numerical validation cases on three GPU models, paraORA agreed with CPU-reference results within the prespecified tolerance while preserving statistical decisions and rankings. On an RTX 4090, the CPU/GPU runtime ratio for the complete in-memory workflow increased from 1.89 for one query to 60.10 for 100 queries against 141,002 gene sets, including one-time preparation but excluding file writing. In a separate complete file-to-file benchmark, GPU-exact was 2.55-fold faster than CPU execution. An exploratory limma reanalysis showed direction-specific Hallmark enrichment. paraORA is available as a web service and an open-source package.

Background/Objectives: Large language models and automated analytical tools show potential for biomedical text understanding, knowledge integration, and single-cell data interpretation, but interpretations of specific gene regulatory relationships still require empirical grounding. For TF–target relationships, one relevant constraint is whether curated regulatory edges show detectable expression-level statistical evidence in the single-cell data being interpreted, because such evidence may vary across cellular states, conditions, and regulatory mechanisms. We therefore evaluated the statistical visibility of known transcription factor (TF)–target relationships in single-cell expression space to aid the interpretation of regulatory inference and automated-tool outputs. Methods: We performed two complementary analyses: first, testing whether curated TF–target edges showed stronger pair-level associations than matched background gene pairs; and second, assessing whether high-scoring TF–gene pairs corresponded to existing regulatory knowledge and whether their target genes showed pathway coherence. Results: Across four peripheral blood mononuclear cell (PBMC) datasets, four adult tissues, and seven adult cell types, curated TF–target relationships showed weak but reproducible statistical visibility rather than strong separation. For all five association metrics, the mean area under the precision–recall curve (AUPRC) was only slightly above the random-ranking baseline of 0.5. High-scoring pairs were more often supported by curated resources, and their target genes showed context-specific Hallmark pathway coherence. Conclusions: Single-cell expression associations can provide useful but limited statistical clues for TF–target regulation and should be interpreted as complementary rather than definitive regulatory evidence.