计算机科学
卷积神经网络
滑动窗口协议
人工智能
水准点(测量)
源代码
机器学习
深度学习
蛋白质结构预测
模式识别(心理学)
数据挖掘
蛋白质结构
窗口(计算)
物理
操作系统
核磁共振
地理
大地测量学
作者
Shan Lü,Yuguang Li,Xiaofei Nan,Shoutao Zhang
标识
DOI:10.1109/bibm52615.2021.9669435
摘要
Protein-protein interactions are of great importance in the life cycles of living cells. Accurate prediction of the proteinprotein interaction site (PPIs) from protein sequence improves our understanding of protein-protein interaction, contributes to the protein-protein docking and is crucial for drug design. However, practical experimental methods are costly and time-consuming so that many sequence-based computational methods have been developed. Most of those methods employ a sliding window approach, which utilize local neighbor information within a window size. However, they don’t distinguish and use the effect of each individual neighboring residue at different position. We propose a novel sequence-based deep learning method consisting of convolutional neural networks (CNNs) and attention mechanism to improve the performance of PPIs prediction. Our attention-based CNNs captures the different effect of each neighboring residue within a sliding window, and therefore making a better understanding of the local environment of target residue. We employ experiments on several public benchmark datasets. The experimental results demonstrate that our proposed method significantly outperforms the state-of-the-art techniques. The source code can be obtained from https://github.com/biolushuai/attention-based-CNNsfor-PPIs-prediction.
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