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iPro2L-Kresidual:用于序列非线性和上下文挖掘的高性能促进者识别模型

Yanjuan Li¹, Shicai Li², Guojun Sheng²

¹College of Electrical and Information Engineering, Quzhou University, Quzhou 324000, China.

|December 30, 2025

概括

这项研究介绍了iPro2L-Kresidual,这是促进者识别的新型模型. 它在预测促销商网站及其强度方面实现了高准确性,改进了现有的方法.

科学领域:

基因组学就是基因组学.
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:

促使者是调节基因表达的关键非编码DNA序列.
促进体中的异常与癌症,糖尿病和心脏病等疾病有关.
现有的模型在非线性特征提取和上下文关系捕获方面扎,限制了促进者识别性能.

研究的目的:

开发一个先进的模型,用于准确的促销商识别.
为了增强非线性特征和序列上下文关系的提取.
提高发起人识别模型的分类性能.

主要方法:

拟议的 iPro2L-Kresidual 模型整合了剩余结构和 KAN 网络 (Kresidual 模块).
增强的变压器编码器使用封闭的反复单元用于本地和全球上下文特征提取.
实施了规范化的标签平滑交叉损失函数,以实现训练稳定性.

主要成果:

通过5倍交叉验证,通过5倍交叉验证实现了促进者识别的94.28%准确率和促进者强度识别的90.55%.
在独立数据集上表现出强大的概括能力,预测准确率为93.13%.
克雷西杜尔模块改进了非线性序列特征表达,并有效地增强了转换器捕获的序列上下文.

结论:

iPro2L-Kresidual提供了一种新且有效的方法来预测主办方的地点.
该模型克服了现有方法在非线性特征提取和上下文理解方面的局限性.
这项工作为与疾病相关的基因调节研究提供了计算基因组学的重大进步.

关键词:

它是DNA促进剂.生物信息学是一种生物信息学.深度学习是一种深度学习.序列分析分析的序列分析.两个阶段的预测预测.

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