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多Pep-DLCL:通过深度学习识别多功能治疗,使用标签序列对比学习

Ting Li¹, Henghui Fan², Jianping Zhao¹

¹College of Mathematics and Systems Science, Xinjiang University, No. 777 Huarui Road, Shuimogou District, Urumqi, Xinjiang Uygur Autonomous Region 830046, China.

Briefings in bioinformatics

|June 16, 2025

概括

这项研究介绍了MultiPep-DLCL,这是一种用于识别多功能治疗 (MFTP) 的新型深度学习方法. 它通过有效学习序列特征和标签嵌入来增强的识别,优于现有的方法.

科学领域:

计算生物学计算生物学
生物信息学是一种生物信息学.
药物发现药物发现药物发现

背景情况:

鉴定多功能治疗性 (MFTP) 是至关重要的,但由于复杂的标签要求,具有挑战性.
当前的方法往往忽略了氨基酸标签的详细语义和序列标签的相互作用.
准确的MFTP分类对于推进基于的治疗方法至关重要.

研究的目的:

为准确的MFTP分类开发一个先进的深度学习模型.
解决现有方法在捕获细微的标签信息和序列-标签相互作用方面的局限性.
为了提高多功能治疗的识别.

主要方法:

提出了MultiPep-DLCL,这是一个用于MFTP分类的深度学习架构.
使用标签序列融合变压器从序列中学习高质量的标签嵌入.
使用标签序列对比学习来加强特征对应.
集成了一个多标签的焦点子损失功能来处理数据集不平衡.

主要成果:

与现有方法相比,MultiPep-DLCL在MFTP识别方面表现优越.
该模型有效地学习了酸序列内的本地和全球依赖性.
通过利用序信息,成功地挖掘了高质量的标签嵌入.
拟议的损失函数有效地解决了不平衡数据集带来的挑战.

结论:

MultiPep-DLCL在多功能治疗识别领域取得了重大进展.
该方法能够整合序列特征和标签嵌入,为复杂类分类提供了强大的框架.
这种方法有望加速新疗法的发现和开发.

关键词:

相反的学习学习学习.标签嵌入式标签嵌入式多个标签分类的分类.治疗性的识别疗法

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