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DeepAffinity: Interpretable Deep Learning of Compound-Protein Affinity through Unified Recurrent and Convolutional Neural Networks

机译：Deepaftity：通过统一的经常性和卷积神经网络解释复合蛋白亲和力的可解释深度学习

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Mostafa Karimi; Di Wu; Zhangyang Wang; Yang shen;
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1. DeepAffinity: interpretable deep learning of compound-protein affinity through unified recurrent and convolutional neural networks [J] . Karimi Mostafa, Wu Di, Wang Zhangyang, Bioinformatics . 2019,第18期

机译：Deepaftity：通过统一的经常性和卷积神经网络解释复合蛋白亲和力的可解释深度学习
2. Detection of Bleeding Events in Electronic Health Record Notes Using Convolutional Neural Network Models Enhanced With Recurrent Neural Network Autoencoders: Deep Learning Approach [J] . Rumeng Li, Baotian Hu, Feifan Liu, JMIR Medical Informatics . 2019,第1期

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3. Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting [J] . Cui Zhiyong, Henrickson Kristian, Ke Ruimin, IEEE Transactions on Intelligent Transportation Systems . 2020,第11期

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4. An energy-efficient deep learning processor with heterogeneous multi-core architecture for convolutional neural networks and recurrent neural networks [C] . Dongjoo Shin, Jinmook Lee, Jinsu Lee, IEEE Symposium on Low-Power and High-Speed Chips . 2017

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5. Convolutional Recurrent Neural Networks and Attention Mechanisms for Robust Deep Learning [D] . Zheng, Jian . 2019

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6. DeepAffinity: interpretable deep learning of compound–protein affinity through unified recurrent and convolutional neural networks [O] . Mostafa Karimi, Di Wu, Zhangyang Wang, -1

机译：Deepaftity：通过统一的经常性和卷积神经网络解释复合蛋白亲和力的可解释深度学习
7. Detection of Bleeding Events in Electronic Health Record Notes Using Convolutional Neural Network Models Enhanced With Recurrent Neural Network Autoencoders: Deep Learning Approach [O] . Rumeng Li, Baotian Hu, Feifan Liu, 2019

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DeepAffinity: Interpretable Deep Learning of Compound-Protein Affinity through Unified Recurrent and Convolutional Neural Networks

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