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SCBi-GRUCapformer:预测多种类型的核酸结合蛋白 (NABPs) 使用鱼传统的BIGRU囊变压器与特征选择

Paritosh Kumar¹, Akshay Deepak¹

¹Department of Computer Science & Engineering, National Institute of Technology, Patna, Bihar, India.

Computer methods in biomechanics and biomedical engineering

|October 24, 2025

概括

本研究介绍了SCBi-GRUCapformer模型用于准确的结合蛋白预测 (BPP),通过解决DNA结合蛋白 (DBPs) 和RNA结合蛋白 (RBPs) 之间的交叉预测问题来改进现有方法. 这种新的方法在分类不同类型的蛋白质方面取得了很高的准确性.

科学领域:

生物信息学是一种生物信息学.
计算生物学计算生物学
机器学习在蛋白质学中的机器学习

背景情况:

准确识别结合蛋白质至关重要,但由于DNA结合蛋白 (DBPs) 和RNA结合蛋白 (RBPs) 之间的预测不准确性和交叉预测问题而具有挑战性.
现有的预测器经常将DBP错误地归类为RBP,反之亦然,突出了需要改进预测方法的需求.
结构上的相似性和显著的交叉预测率需要一种新的方法来准确地预测结合蛋白 (BPP).

研究的目的:

提出一种新且准确的结合蛋白预测 (BPP) 技术,即Squash卷积型BiGRU囊变压器 (SCBi-GRUCapformer) 模型.
为了提高预测准确度,减少不同类型的结合蛋白之间的交叉预测错误.
准确地将蛋白质序列分为四类:DBPs,RBPs,DRBPs和非结合蛋白 (NNABP).

主要方法:

一个四阶段的方法:预处理 (删除短序 <40 aa),特征提取 (使用 protlearn 进行 ATC, DPC, AAC, PCP),特征选择 (使用学习高山滑雪模拟优化 - LASIO) 和预测.
利用SCBi-GRUCapformer模型,集成卷积层,BiGRU,囊网络和变压器进行序列分析.
通过准确性,精度,回忆和F测量,以及除去研究和交叉验证来评估性能.

主要成果:

该SCBi-GRUCapformer模型实现了高性能指标:97.84%的准确性,95.27%的精度,95.14%的回忆和95.2%的F测量.
在区分DBPs,RBPs,DRBPs和NNABP (分别为0,1,2和3类) 中表现出卓越的性能.
废弃性研究和交叉验证证实了拟议的SCBi-GRUCapformer模型的有效性和稳定性.

结论:

SCBi-GRUCapformer模型代表了结合蛋白预测 (BPP) 的重大进步,克服了以前方法的局限性.
拟议的方法为将蛋白质序列分为功能类别提供了强大而准确的解决方案.
这项工作为蛋白质组学和分子生物学研究人员提供了宝贵的工具,这些研究人员需要精确识别结合蛋白质.

关键词:

它们是DNA DNA DNA DNA.这是一个RNARNARNARNARNA.结合蛋白质的结合蛋白质.囊变压器囊变压器卷积式学习是一种卷积式学习.功能选择功能选择

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