Gradient Boosting Decision Tree-Based Method for Predicting Interactions Between Target Genes and Drugs

Xuan, Ping; Sun, Chang; Zhang, Tiangang; Ye, Yilin; Shen, Tonghui; Dong, Yihua

doi:10.3389/fgene.2019.00459

Cited by 72 publications

(30 citation statements)

References 77 publications

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“…It is not difficult to surmise that there is a serious imbalance between positive samples and negative samples. In this case, the PR (precision-recall) curve usually reflects more information than the ROC curve [35,36]. Precision indicates the proportion of positive samples that are defined correctly compared to the number of positive samples currently defined as positive examples.…”

Section: Resultsmentioning

confidence: 99%

Inferring the Disease-Associated miRNAs Based on Network Representation Learning and Convolutional Neural Networks

Xuan

Sun

Wang

et al. 2019

IJMS

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Identification of disease-associated miRNAs (disease miRNAs) are critical for understanding etiology and pathogenesis. Most previous methods focus on integrating similarities and associating information contained in heterogeneous miRNA-disease networks. However, these methods establish only shallow prediction models that fail to capture complex relationships among miRNA similarities, disease similarities, and miRNA-disease associations. We propose a prediction method on the basis of network representation learning and convolutional neural networks to predict disease miRNAs, called CNNMDA. CNNMDA deeply integrates the similarity information of miRNAs and diseases, miRNA-disease associations, and representations of miRNAs and diseases in low-dimensional feature space. The new framework based on deep learning was built to learn the original and global representation of a miRNA-disease pair. First, diverse biological premises about miRNAs and diseases were combined to construct the embedding layer in the left part of the framework, from a biological perspective. Second, the various connection edges in the miRNA-disease network, such as similarity and association connections, were dependent on each other. Therefore, it was necessary to learn the low-dimensional representations of the miRNA and disease nodes based on the entire network. The right part of the framework learnt the low-dimensional representation of each miRNA and disease node based on non-negative matrix factorization, and these representations were used to establish the corresponding embedding layer. Finally, the left and right embedding layers went through convolutional modules to deeply learn the complex and non-linear relationships among the similarities and associations between miRNAs and diseases. Experimental results based on cross validation indicated that CNNMDA yields superior performance compared to several state-of-the-art methods. Furthermore, case studies on lung, breast, and pancreatic neoplasms demonstrated the powerful ability of CNNMDA to discover potential disease miRNAs.

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Section: Resultsmentioning

confidence: 99%

Inferring the Disease-Associated miRNAs Based on Network Representation Learning and Convolutional Neural Networks

Xuan

Sun

Wang

et al. 2019

IJMS

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“…We calculate different true positive rates (TPRs), false positive rates (FPRs), accuracy (precisions), and recall (recall) in each θ as follows TPR=TPTP+FN,FPR=FPTN+FP, precision=TPTP+FP,recall=TPTP+FN where TP indicates the correct identification of the number of positive samples, TN indicates the correct identification of the number of negative samples, FP indicates the number of samples that will be predicted as a positive example, and FN indicates the number of samples identified as a negative sample. Thus, the receiver operating characteristic (ROC) curve [36] can be drawn using different TPRs and FPRs under different θ . The area under the curve (AUC) is called the drug-related AUC value.…”

Section: Resultsmentioning

confidence: 99%

Inferring Drug-Related Diseases Based on Convolutional Neural Network and Gated Recurrent Unit

et al. 2019

Self Cite

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Predicting novel uses for drugs using their chemical, pharmacological, and indication information contributes to minimizing costs and development periods. Most previous prediction methods focused on integrating the similarity and association information of drugs and diseases. However, they tended to construct shallow prediction models to predict drug-associated diseases, which make deeply integrating the information difficult. Further, path information between drugs and diseases is important auxiliary information for association prediction, while it is not deeply integrated. We present a deep learning-based method, CGARDP, for predicting drug-related candidate disease indications. CGARDP establishes a feature matrix by exploiting a variety of biological premises related to drugs and diseases. A novel model based on convolutional neural network (CNN) and gated recurrent unit (GRU) is constructed to learn the local and path representations for a drug-disease pair. The CNN-based framework on the left of the model learns the local representation of the drug-disease pair from their feature matrix. As the different paths have discriminative contributions to the drug-disease association prediction, we construct an attention mechanism at the path level to learn the informative paths. In the right part, a GRU-based framework learns the path representation based on path information between the drug and the disease. Cross-validation results indicate that CGARDP performs better than several state-of-the-art methods. Further, CGARDP retrieves more real drug-disease associations in the top part of the prediction result that are of concern to biologists. Case studies on five drugs demonstrate that CGARDP can discover potential drug-related disease indications.

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“…We performed 5 fold cross-validation 20 times to evaluate the performance of our prediction method and the corresponding results were averaged [31,32]. First, known associated drug–disease pairs were divided randomly into five subsets and treated as positive samples.…”

Section: Experimental Evaluation and Discussionmentioning

confidence: 99%

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

Xuan

Zhang

et al. 2019

Cells

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Identifying novel indications for approved drugs can accelerate drug development and reduce research costs. Most previous studies used shallow models for prioritizing the potential drug-related diseases and failed to deeply integrate the paths between drugs and diseases which may contain additional association information. A deep-learning-based method for predicting drug–disease associations by integrating useful information is needed. We proposed a novel method based on a convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM)—CBPred—for predicting drug-related diseases. Our method deeply integrates similarities and associations between drugs and diseases, and paths among drug-disease pairs. The CNN-based framework focuses on learning the original representation of a drug-disease pair from their similarities and associations. As the drug-disease association possibility also depends on the multiple paths between them, the BiLSTM-based framework mainly learns the path representation of the drug-disease pair. In addition, considering that different paths have discriminate contributions to the association prediction, an attention mechanism at path level is constructed. Our method, CBPred, showed better performance and retrieved more real associations in the front of the results, which is more important for biologists. Case studies further confirmed that CBPred can discover potential drug-disease associations.

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Gradient Boosting Decision Tree-Based Method for Predicting Interactions Between Target Genes and Drugs

Cited by 72 publications

References 77 publications

Inferring the Disease-Associated miRNAs Based on Network Representation Learning and Convolutional Neural Networks

Inferring the Disease-Associated miRNAs Based on Network Representation Learning and Convolutional Neural Networks

Inferring Drug-Related Diseases Based on Convolutional Neural Network and Gated Recurrent Unit

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

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