Mating strategies in Caenorhabditis elegans populations are determined by male developmental history.

Gene set interpretation is a fundamental task in functional genomics, where researchers must derive biological insights from lists of genes identified in high-throughput experiments. Current approaches utilise statistical enrichment methods that query predefined functional databases, such as Gene Ontology [] and KEGG pathways [], to identify overrepresented biological processes [,]. Although powerful, current methods yield fragmented outputs, such as lists of enriched terms from various ontologies, leaving researchers to manually integrate these results to achieve functional insights, a process that is both inefficient and error-prone. Exploratory gene set analysis has become increasingly challenging as the volume of annotated datasets grows. Researchers compare their findings not only with gene ontology term enrichments but also with signatures from knockdown experiments and diverse resources such as LINCS (Library of Integrated Network-based Cellular Signatures) [] and the STRING database (STRING-DB) []. The main issue is that enrichment analysis tests for the over-representation of genes associated with specific terms. When gene lists overlap, the same genes often appear under many different functional terms. As a result, enrichment analysis can return many distinct-sounding terms that all point to the same underlying biology, making interpretation more difficult. However, the challenge extends beyond simply removing duplicate information. Simplistic filtering of overlapping gene sets would obscure important biological relationships that only become apparent when analysing genes across the diverse resources mentioned above. These relationships often represent biological processes that bridge multiple databases and reveal insights not captured by any single resource. Consequently, manually curating enrichment outputs to identify both redundancies and meaningful biological patterns is not only error-prone and prohibitively time-consuming but also risks overlooking crucial biological connections.