TPdsm: a method based on TabPFN for prediction of deleterious synonymous mutations

Motivation Assessing the deleteriousness of synonymous mutations is of considerable importance for understanding human health, and the development of corresponding prediction methods offers a rapid and efficient approach. However, the scarcity of training data remains a major bottleneck in building high-performance predictors for synonymous mutation deleteriousness. Herein, we present TPdsm, a novel method for predicting deleterious synonymous mutations. Results TPdsm leverages TabPFN, a tabular foundation model specifically designed for small-sample prediction. The features are retrieved from CDsyn, a comprehensive database dedicated to deleterious synonymous mutation prediction. Our evaluation demonstrates that TPdsm delivers better predictive performance than a set of 14 current state-of-the-art predictors, as evidenced by its results across multiple independent testing datasets and real-world cases. Availability and implementation TPdsm is available on GitHub at https://github.com/Project4bz2023/TPdsm. The pre-computed score of TPdsm can be accessed at https://doi.org/10.5281/zenodo.18265619. The result can be queried at https://bingtseng-tpdsm.share.connect.posit.cloud/.