Confidence-aware learning for transcriptome-based prediction of OXPHOS genes in Caenorhabditis elegans under incomplete functional annotation

Assigning biological functions to genes remains a major challenge in genomics because reliable functional annotations are often scarce and unevenly distributed. Although transcriptomic datasets capture rich information about gene activity and coordination, exploiting these data for gene function prediction is difficult when only a small number of genes have high-confidence functional assignments and the remaining labels are uncertain. Here, we developed a confidence-aware learning framework that integrates complementary transcriptomic landscapes while explicitly incorporating uncertainty in functional annotations. The framework combines supervised learning from time-resolved bulk RNA-seq data with co-expression analysis of embryonic and adult single-cell transcriptomes. Supervised learning uses a two-round training scheme in which genes supported by limited functional evidence are incorporated only after an initial model has been established using high-confidence annotations. To reduce module-specific biases, we implemented an informed bagging strategy in which genes from individual functional modules are systematically withheld during training and predictions are integrated by consensus across models. We applied this framework to identify genes involved in oxidative phosphorylation (OXPHOS) in Caenorhabditis elegans. Integrating supervised and co-expression evidence prioritized a small set of high-confidence candidate genes with strong predictive performance on an independent test set. Experimental validation showed that disruption of the top-ranked candidate, ril-1, produces phenotypes consistent with impaired OXPHOS function. Our results demonstrate that transcriptomic landscapes can be systematically exploited for gene function prediction under incomplete functional annotation, providing a framework that may be transferable to other biological processes and organisms where reliable annotations remain limited.