CNN-Siam uses a convolutional neural network (CNN) as a backbone network in the form of a twin network architecture to learn the feature representation of drug pairs from multimodal data of drugs (including chemical substructures, targets and enzymes).
Mar 23, 2023
Mar 20, 2023 · Moreover, this network is used to predict the types of drug interactions with the best optimization algorithms available (RAdam and LookAhead).
Feb 19, 2024 · Our Multi-Modal Convolutional Neural Network - Drug to Drug Interaction (MCNN-DDI) model achieved an accuracy of 90.00% and an AUPR of 94.78%.
Missing: Siam: | Show results with:Siam:
Table 3 Results of different optimizers. From: CNN-Siam: multimodal siamese CNN-based deep learning approach for drug‒drug interaction prediction. Optimizer.
It is found that a Siamese multi-modal neural network is able to accurately predict DDIs and that an Attention mechanism can be beneficially applied to aid ...
CNN-Siam learns a representation for an individual drug by feeding its chemical substructure, target, and enzyme information into two CNNs with shared ...
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Feb 22, 2024 · Conclusion The experimental results show that our multimodal siamese convolutional neural network can accurately predict DDIs, and the Siamese ...
CNN-Siam: multimodal siamese CNN-based deep learning approach for drug‒drug interaction prediction. Zihao Yang, Kuiyuan Tong, Shiyu Jin, Shiyan Wang, Chao ...
Mar 23, 2023 · Any future updates will be listed below. CNN-Siam: multimodal siamese CNN-based deep learning approach for drug‒drug interaction prediction.
Cheng, F., Zhao, Z.: Machine learning-based prediction ... CNN-Siam: multimodal siamese CNN-based deep learning approach for drug‒drug interaction prediction.