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

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ID: 320984
2026
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Abstract
Abstract 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/.
Reference Key
openalex_W7168256940 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Bing Zeng, Min Hu, Zhonghui Cui, Siting Zhou, Weiwei Dai
Journal Bioinformatics advances
Year 2026
DOI
10.1093/bioadv/vbag194
URL
https://doi.org/10.1093/bioadv/vbag194
Keywords Keywords not found

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