Accurate prediction of oligonucleotide probe hybridization efficiency via a transformer-based model.

Gene detection chip design critically depends on oligonucleotide probe hybridization efficiency, yet conventional thermodynamic models for its evaluation diverge from experiments, and the lack of comprehensive universal datasets further stops models from reflecting real probe behaviors. To address these issues, a dataset with 8 subsets (over 3.8 million probes) was first constructed by integrating public microarray data. We then propose TransHybrid-a sequence-based deep learning framework that embeds complementary strand features into a Transformer architecture. The validation and test results show that the performance of TransHybrid, as measured by the Pearson correlation coefficient (PCC), is more than double that of traditional thermodynamic models, significantly outperforming conventional evaluation methods. The robustness of our model was validated via our independent experimental dataset, which was constructed through a series of designed experiments for fine-tuning TransHybrid. Following fine-tuning on this custom dataset, TransHybrid achieved a 25% improvement over the PCC of the thermodynamic models. Furthermore, mechanistic analyses revealed that TransHybrid captures biologically meaningful sequence features, such as the contribution of GC-rich regions, and provides interpretable attention maps. These findings demonstrate the superior ability of TransHybrid to accurately model hybridization efficiency, paving the way for more effective probe design in gene detection applications.