MOSTPLAS:一种自我校正的多标签学习模型,用于对等离子体宿主范围的预测
Wei Zou1, Yongxin Ji1, Jiaojiao Guan1
1Department of Electrical Engineering, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong SAR, China.
Bioinformatics (Oxford, England)
|February 17, 2025
概括
一个新的自我校正模型,MOSTPLAS,准确地预测等离子体宿主范围,即使有不完整的数据. 这促进了对细菌进化和水平基因转移的理解.
科学领域:
- 微生物学 微生物学
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 等离子体是细菌进化的关键驱动力,通过横向基因转移,赋予了抗生素耐药性等特征.
- 广域宿主等离子体可以在多种生物中存在,使得它们的宿主范围的识别对于理解细菌适应至关重要.
- 目前对综合性等离子体宿主范围数据的局限性阻碍了准确的预测模型的开发.
研究的目的:
- 开发一种可靠的计算模型,用于预测宽主机范围等离子体的宿主范围.
- 为了应对等离子体宿主范围数据集中不完整和缺失的标签数据的挑战.
- 为了提高等离子体宿主范围预测的准确性和全面性.
主要方法:
- 提出了一个名为MOSTPLAS的新型自我纠正多标签学习模型.
- 实现了伪标签学习算法和自我纠正不对称损失函数来处理缺失的标签.
- 使用各种数据集验证模型,包括NCBI RefSeq,PLSDB 2025和实验确定的主机标签.
主要成果:
- 与现有的工具相比,MOSTPLAS在识别更多的主机标签方面表现出卓越的性能.
- 该模型在预测中保持了高精度,表明了可靠性.
- 实验结果证实了自我纠正机制在处理不完整数据方面的有效性.
结论:
- 在预测等离子体宿主范围方面,MOSTPLAS提供了显著的进步,特别是对于宽宿主范围等离子体.
- 该模型处理缺失标签的能力克服了现有方法中的关键瓶.
- 这项工作为研究塑体生物学和细菌进化研究的研究人员提供了宝贵的工具.
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