吉林大学学报(工学版) ›› 2022, Vol. 52 ›› Issue (1): 187-194.doi: 10.13229/j.cnki.jdxbgxb20200723
• 计算机科学与技术 • 上一篇
Gui-xia LIU1,2(),Zhi-yao PEI1,2,Jia-zhi SONG1,2
摘要:
为了提高识别蛋白质-ATP结合位点预测精度,提出了基于Inception架构的深度网络模型Inception_base,同时对网络模型和训练策略进行优化和改进,提出了新的网络模型Inception_evolution。通过两组数据集在该模型上测试,获得AUC分别为0.885和0.918,均优于其他对比机器学习方法。实验结果表明,深度学习方法可以应用于蛋白质-ATP结合位点预测问题中,该模型能够更精确预测蛋白质-ATP结合位点。
中图分类号:
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