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MCMFPP:一种基于类特征增强和分类器融合的多功能预测方法

Jintao Zhao¹, Henghui Fan², Jiwei Fang¹

¹College of Mathematics and System Sciences, Xinjiang University, Xinjiang, 830017, China.

Journal of chemical information and modeling

|September 18, 2025

概括

开发准确的计算工具来预测功能至关重要. 新的MCMFPP方法通过整合序列和类特征学习来提高多功能治疗 (MFTP) 的预测准确性.

科学领域:

* 计算生物学和生物信息学.
* 药物发现和开发.
* 体科学和治疗学.

背景情况:

* 传统的湿实验室方法用于功能预测是耗时且昂贵的.
* 现有的计算工具在数据稀疏性,不平衡的数据和多功能治疗 (MFTP) 的特征表示方面扎.
*准确的MFTP预测对于开发向类药物至关重要.

研究的目的:

* 开发一种先进的计算方法,用于准确的多功能预测.
* 解决现有方法的局限性,包括数据稀疏性和不充分的特征表示.
* 提高用于治疗应用的候选的识别效率和准确性.

主要方法:

* 引入两个子分类器:序列学习特征增强 (SLFE) 和类特征增强分类器 (CFEC).
*SLFE使用ESMC大型语言模型来增强序列表示,特别是尾部类数据.
*CFEC通过使用单功能样本和对比学习来改善类特征学习.
* MCMFPP的提议,这是一个深度学习模型,通过加权融合集成SLFE和CFEC预测.

主要成果:

* MCMFPP在多功能预测方面表现优于最先进的方法.
*取得的改进包括3.1%的精度,2.7%的覆盖率,2.8%的准确性和2.7%的绝对真实率.
* 降低了0.3%的绝对虚假率.
* 提高了对具有挑战性的多功能样品的预测准确性.

结论:

*MCMFPP有效地克服了MFTP预测中单个分类器方法的局限性.
* 拟议的方法为有效和准确的多功能治疗的识别提供了有价值的工具.
* 这一进步可以加速开发新的类向药物.

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