S-DCNN:基于SMOTE的深卷积神经网络对ATP结合残留物的预测
Sixi Hao1,2, Cai-Yan Li3, Xiuzhen Hu1
1College of Sciences, Inner Mongolia University of Technology, Hohhot, China.
Frontiers in genetics
|January 21, 2025
概括
本研究介绍了S-DCNN,这是一种用于预测ATP结合残留物的深度学习方法. 通过结合额外的功能和过量采样技术,S-DCNN提高了预测准确度.
科学领域:
- 生物化学 生物化学
- 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:
- 蛋白质的功能往往取决于联体结合.
- 腺三酸盐 (ATP) 是一种关键的蛋白质结合配体,参与许多生物过程.
- 准确预测ATP结合残留物目前是一个重大挑战.
研究的目的:
- 开发和评估一种基于序列信息预测ATP结合残留的新计算方法.
- 为了提高ATP结合部位预测的准确性和可靠性.
主要方法:
- 一个深度卷积神经网络 (DCNN) 模型被开发用于ATP结合残留预测.
- 该模型被称为S-DCNN,并使用合成少数人过量采样技术 (SMOTE) 进行了增强.
- 额外的特征参数包括二面角,能量和倾向因子被整合到模型中.
主要成果:
- 该S-DCNN模型在ATP-289数据集上显著提高了预测准确性.
- 该方法在一个独立的测试组中实现了0.5031的马修斯相关系数和97.06%的准确性.
- 在ATP-221数据集上,S-DCNN的性能优于现有的方法,在ATP-388数据集上表现相似.
结论:
- S-DCNN方法有效地准确预测ATP结合残留物.
- 这种方法提供了一个强大的计算工具,用于识别蛋白质中的ATP结合位.
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