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xBitterT5:一个可解释的基于变压器的框架,具有多模式输入,用于识别苦味
Nguyen Doan Hieu Nguyen1, Nhat Truong Pham1, Duong Thanh Tran1
1Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea.
Journal of cheminformatics
|August 20, 2025
概括
使用xBitterT5,一种新的多式模式框架来识别苦 (BPs). 这种方法整合了序列和分子表示,以提高食品科学和生物医学中的准确性和可解释性.
科学领域:
- 食品科学 食品科学 食品科学
- 生物医学是生物医学.
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
背景情况:
- 苦 (BPs) 影响口味和生理过程,对食品质量和健康至关重要.
- 对于BP识别的传统机器学习方法在复杂的生物序列数据方面存在局限性.
- 蛋白质语言模型的最新进展提供了更好的准确性,但往往缺乏可解释性和分子表示性.
研究的目的:
- 开发一种新的,可解释的框架,用于准确识别苦.
- 整合序列和分子表示,以提高预测能力.
- 提供对酸苦味的化学基础的机制性见解.
主要方法:
- 提出了xBitterT5,一个多式框架,将BioT5嵌入与序列和SELFIES分子表示相结合.
- 使用预训练过的基于变压器的嵌入式进行高级特征提取.
- 整合了序列和分子字符串数据,以便进行全面分析.
主要成果:
- 与基准数据集上的现有方法相比,xBitterT5实现了更高的性能.
- 该模型证明了残留水平的解释性,识别了导致苦味的关键化学子结构.
- 提供了超越传统黑子预测模型的机械洞察力.
结论:
- xBitterT5在苦味的识别方面提供了显著的进步.
- 该框架为基于的食品和生物医学应用提供了有价值的机制见解.
- 自由可用的Web服务器和独立版本促进了更广泛的研究可访问性.
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