Authors
Bing Zeng, Min Hu, Zhonghui Cui, Siting Zhou, Weiwei Dai
Published in
Bioinformatics advances. Volume 6. Issue 1. Pages vbag194. Epub Jul 13, 2026.
Abstract
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.
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.
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/.
PMID:
42502480
Bibliographic data and abstract were imported from PubMed on 26 Jul 2026.
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