Convolutional Neural Network and Bidirectional Long Short-Term Memory-Based Method for Predicting Drug–Disease Associations

Xuan, Ping; Ye, Yilin; Zhang, Tiangang; Zhao, Lianfeng; Sun, Chang

doi:10.3390/cells8070705

Cited by 40 publications

(13 citation statements)

References 40 publications

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“…The true positive rates (TPRs) and the false positive rates (FPRs) at various θ values are calculated as follows: TPR=TPTP+FN, FPR=FPFP+TN where TP and TN are the numbers of positive and negative samples that are identified correctly, while FN and FP are the numbers of misidentified positive and negative samples. The receiver operating characteristic (ROC) curve can be drawn according to the TPRs and FPRs at each various θ, while the area under the ROC curve (AUC) is usually used to evaluate the overall performance of a prediction method [17].…”

Section: Experimental Evaluations and Discussionmentioning

confidence: 99%

CNNDLP: A Method Based on Convolutional Autoencoder and Convolutional Neural Network with Adjacent Edge Attention for Predicting lncRNA–Disease Associations

Xuan

Sheng

Zhang

et al. 2019

IJMS

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It is well known that the unusual expression of long non-coding RNAs (lncRNAs) is closely related to the physiological and pathological processes of diseases. Therefore, inferring the potential lncRNA–disease associations are helpful for understanding the molecular pathogenesis of diseases. Most previous methods have concentrated on the construction of shallow learning models in order to predict lncRNA-disease associations, while they have failed to deeply integrate heterogeneous multi-source data and to learn the low-dimensional feature representations from these data. We propose a method based on the convolutional neural network with the attention mechanism and convolutional autoencoder for predicting candidate disease-related lncRNAs, and refer to it as CNNDLP. CNNDLP integrates multiple kinds of data from heterogeneous sources, including the associations, interactions, and similarities related to the lncRNAs, diseases, and miRNAs. Two different embedding layers are established by combining the diverse biological premises about the cases that the lncRNAs are likely to associate with the diseases. We construct a novel prediction model based on the convolutional neural network with attention mechanism and convolutional autoencoder to learn the attention and the low-dimensional network representations of the lncRNA–disease pairs from the embedding layers. The different adjacent edges among the lncRNA, miRNA, and disease nodes have different contributions for association prediction. Hence, an attention mechanism at the adjacent edge level is established, and the left side of the model learns the attention representation of a pair of lncRNA and disease. A new type of lncRNA similarity and a new type of disease similarity are calculated by incorporating the topological structures of multiple bipartite networks. The low-dimensional network representation of the lncRNA-disease pairs is further learned by the autoencoder based convolutional neutral network on the right side of the model. The cross-validation experimental results confirm that CNNDLP has superior prediction performance compared to the state-of-the-art methods. Case studies on stomach cancer, breast cancer, and prostate cancer further show the ability of CNNDLP for discovering the potential disease lncRNAs.

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Section: Experimental Evaluations and Discussionmentioning

confidence: 99%

CNNDLP: A Method Based on Convolutional Autoencoder and Convolutional Neural Network with Adjacent Edge Attention for Predicting lncRNA–Disease Associations

Xuan

Sheng

Zhang

et al. 2019

IJMS

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“…Meanwhile, it showcased in two case studies that the top drug candidates predicted by SNF-CVAE can potentially treat Alzheimer's disease and Juvenile rheumatoid arthritis, which were successfully validated by clinical trials and published studies. Furthermore, Xuan et al [104] proposed a novel method based on CNN and bidirectional LSTM for drug repurposing, where the CNN-based module was used to learn the original representation of drug-disease pairs from their similarities and associations; yet, the BiLSTM-based module was used to learn the path representations of the drug-disease to balance the contributions of different paths by attention mechanism.…”

Section: Disease-centered Modelsmentioning

confidence: 99%

Deep learning for drug repurposing: Methods, databases, and applications

Pan

Lin

Cao

et al. 2022

WIREs Comput Mol Sci

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Drug development is time-consuming and expensive. Repurposing existing drugs for new therapies is an attractive solution that accelerates drug development at reduced experimental costs, specifically for Coronavirus Disease 2019 (COVID-19), an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, comprehensively obtaining and productively integrating available knowledge and big biomedical data to effectively advance deep learning models is still challenging for drug repurposing in other complex diseases. In this review, we introduce guidelines on how to utilize deep learning methodologies and tools for drug repurposing. We first summarized the commonly used bioinformatics and pharmacogenomics databases for drug repurposing. Next, we discuss recently developed sequence-based and graph-based representation approaches as well as state-of-the-art deep learning-based methods. Finally, we present applications of drug repurposing to fight the COVID-19 pandemic, and outline its future challenges.

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“…e higher the attention score, the higher the matching degree between the input vector and the target vector; ω, A and b represent the weight matrix and bias of the attention model, respectively. According to the attention distribution a t , the input vector of the attention layer is weighted and summed to obtain the output vector c of the attention model [37]:…”

Section: Self-attention Mechanismmentioning

confidence: 99%

Prediction Method of TBM Tunneling Parameters Based on Bi‐GRU‐ATT Model

Zhang

Yang

Zhu

et al. 2022

Advances in Civil Engineering

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With tunnel boring machines (TBMs) widely used in tunnel construction, the adaptable adjustment of TBM operating status has become a research focus. Since the prediction of tunnel geological conditions is still challenging before excavating, the prediction of important TBM operating parameters plays an important role in the research on TBM adaptable adjustment. This paper proposes an intelligent prediction method of TBM tunneling parameters based on bidirectional gate recurrent unit incorporating attention mechanism (Bi-GRU-ATT) and selects a complete tunneling cycle to predict the tunneling parameters of the TBM complete tunneling cycle. Relying on the TBM3 bid section of Jilin Water Supply Project, 21 key parameters of the complete tunneling cycle are selected as the input features of the model to realize the prediction of four tunneling parameters in the complete driving cycle section of TBM. Compared with the Bi-GRU, GRU, and Long Short-Term Memory (LSTM) models, it can be seen that the Bi-GRU-ATT model has a goodness of fit for predicting TBM tunneling parameters above 0.92, and the average absolute percentage error is less than 1.8%. The results show that the prediction method of TBM tunneling parameters based on Bi-GRU-ATT model proposed in this paper has stronger learning and prediction capabilities. This prediction method provides a more feasible auxiliary intelligent decision-making method for TBM aided intelligent construction.

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Convolutional Neural Network and Bidirectional Long Short-Term Memory-Based Method for Predicting Drug–Disease Associations

Cited by 40 publications

References 40 publications

CNNDLP: A Method Based on Convolutional Autoencoder and Convolutional Neural Network with Adjacent Edge Attention for Predicting lncRNA–Disease Associations

CNNDLP: A Method Based on Convolutional Autoencoder and Convolutional Neural Network with Adjacent Edge Attention for Predicting lncRNA–Disease Associations

Deep learning for drug repurposing: Methods, databases, and applications

Prediction Method of TBM Tunneling Parameters Based on Bi‐GRU‐ATT Model

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