iProtDNA-SMOTE:通过不平衡的图形神经网络增强蛋白质-DNA结合点预测
Ruiyan Huang1, Wangren Qiu1, Xuan Xiao1,2
1School of Information Engineering, Jingdezhen Ceramic University, Jingdezhen Jiangxi, China.
PloS one
|May 13, 2025
概括
我们开发了iProtDNA-SMOTE,这是一种使用图形神经网络和蛋白质语言模型准确预测蛋白质-DNA结合位点的新方法. 这种方法提高了预测准确性和概括性,解决了生物数据中阶级不平衡的挑战.
科学领域:
- 分子生物学分子生物学
- 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-DNA相互作用是细胞过程的基础,包括基因调节和DNA修复.
- 对DNA结合残留的准确预测对于了解蛋白质功能和疾病机制至关重要.
- 现有的方法在预测蛋白质-DNA结合位点时,经常与阶级不平衡作斗争.
研究的目的:
- 开发和验证一种新的计算方法,iProtDNA-SMOTE,用于预测蛋白质中的DNA结合残留物.
- 为了应对蛋白质-DNA结合部位预测数据集中的类失衡的挑战.
- 为了提高DNA结合部位预测的准确性,概括性和特异性.
主要方法:
- 利用与预先训练的蛋白质语言模型集成的非平衡图神经网络 (GNNs).
- 实现了SMOTE (合成少数人过量采样技术) 算法来处理不平衡的图形数据.
- 在已建立的数据集 (TR646,TR573) 和独立的测试集 (TE46,TE129,TE181) 上训练和评估模型.
主要成果:
- 实现了高的曲线下的面积 (AUC) 值:0.850 (TE46),0.896 (TE129) 和0.858 (TE181).
- 与现有方法相比,在预测DNA结合位点方面表现出优越的性能.
- 由于对不平衡数据的有效处理,展示了增强的模型概括性和特异性.
结论:
- iProtDNA-SMOTE提供了一种可靠和准确的方法来预测蛋白质-DNA结合部位.
- 这种方法有效地克服了阶级不平衡问题,从而提高了预测性能.
- 公开可用的代码和数据集促进了对蛋白质-DNA相互作用预测的进一步研究.
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