计算机科学
卷积神经网络
人工智能
深度学习
边距(机器学习)
代表(政治)
特征学习
网络分析
机器学习
鉴定(生物学)
特征(语言学)
图形
理论计算机科学
生物
法学
哲学
政治
物理
量子力学
植物
语言学
政治学
作者
Tianyi Zhao,Yang Hu,Linda R. Valsdottir,Tianyi Zang,Jiajie Peng
摘要
Abstract Identification of new drug–target interactions (DTIs) is an important but a time-consuming and costly step in drug discovery. In recent years, to mitigate these drawbacks, researchers have sought to identify DTIs using computational approaches. However, most existing methods construct drug networks and target networks separately, and then predict novel DTIs based on known associations between the drugs and targets without accounting for associations between drug–protein pairs (DPPs). To incorporate the associations between DPPs into DTI modeling, we built a DPP network based on multiple drugs and proteins in which DPPs are the nodes and the associations between DPPs are the edges of the network. We then propose a novel learning-based framework, ‘graph convolutional network (GCN)-DTI’, for DTI identification. The model first uses a graph convolutional network to learn the features for each DPP. Second, using the feature representation as an input, it uses a deep neural network to predict the final label. The results of our analysis show that the proposed framework outperforms some state-of-the-art approaches by a large margin.
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