Aberrant expressions of long non-coding RNAs (lncRNAs) are often associated with diseases and identification of disease-related lncRNAs is helpful for elucidating complex pathogenesis. Recent methods for predicting associations between lncRNAs and diseases integrate their pertinent heterogeneous data. However, they failed to deeply integrate topological information of heterogeneous network comprising lncRNAs, diseases, and miRNAs. We proposed a novel method based on the graph convolutional network and convolutional neural network, referred to as GCNLDA, to infer disease-related lncRNA candidates. The heterogeneous network containing the lncRNA, disease, and miRNA nodes, is constructed firstly. The embedding matrix of a lncRNA-disease node pair was constructed according to various biological premises about lncRNAs, diseases, and miRNAs. A new framework based on a graph convolutional network and a convolutional neural network was developed to learn network and local representations of the lncRNA-disease pair. On the left side of the framework, the autoencoder based on graph convolution deeply integrated topological information within the heterogeneous lncRNA-disease-miRNA network. Moreover, as different node features have discriminative contributions to the association prediction, an attention mechanism at node feature level is constructed. The left side learnt the network representation of the lncRNA-disease pair. The convolutional neural networks on the right side of the framework learnt the local representation of the lncRNA-disease pair by focusing on the similarities, associations, and interactions that are only related to the pair. Compared to several state-of-the-art prediction methods, GCNLDA had superior performance. Case studies on stomach cancer, osteosarcoma, and lung cancer confirmed that GCNLDA effectively discovers the potential lncRNA-disease associations.

Determining the target genes that interact with drugs—drug–target interactions—plays an important role in drug discovery. Identification of drug–target interactions through biological experiments is time consuming, laborious, and costly. Therefore, using computational approaches to predict candidate targets is a good way to reduce the cost of wet-lab experiments. However, the known interactions (positive samples) and the unknown interactions (negative samples) display a serious class imbalance, which has an adverse effect on the accuracy of the prediction results. To mitigate the impact of class imbalance and completely exploit the negative samples, we proposed a new method, named DTIGBDT, based on gradient boosting decision trees, for predicting candidate drug–target interactions. We constructed a drug–target heterogeneous network that contains the drug similarities based on the chemical structures of drugs, the target similarities based on target sequences, and the known drug–target interactions. The topological information of the network was captured by random walks to update the similarities between drugs or targets. The paths between drugs and targets could be divided into multiple categories, and the features of each category of paths were extracted. We constructed a prediction model based on gradient boosting decision trees. The model establishes multiple decision trees with the extracted features and obtains the interaction scores between drugs and targets. DTIGBDT is a method of ensemble learning, and it effectively reduces the impact of class imbalance. The experimental results indicate that DTIGBDT outperforms several state-of-the-art methods for drug–target interaction prediction. In addition, case studies on Quetiapine, Clozapine, Olanzapine, Aripiprazole, and Ziprasidone demonstrate the ability of DTIGBDT to discover potential drug–target interactions.

A lot of studies indicated that aberrant expression of long non-coding RNA genes (lncRNAs) is closely related to human diseases. Identifying disease-related lncRNAs (disease lncRNAs) is critical for understanding the pathogenesis and etiology of diseases. Most of the previous methods focus on prioritizing the potential disease lncRNAs based on shallow learning methods. The methods fail to extract the deep and complex feature representations of lncRNA-disease associations. Furthermore, nearly all the methods ignore the discriminative contributions of the similarity, association, and interaction relationships among lncRNAs, disease, and miRNAs for the association prediction. A dual convolutional neural networks with attention mechanisms based method is presented for predicting the candidate disease lncRNAs, and it is referred to as CNNLDA. CNNLDA deeply integrates the multiple source data like the lncRNA similarities, the disease similarities, the lncRNA-disease associations, the lncRNA-miRNA interactions, and the miRNA-disease associations. The diverse biological premises about lncRNAs, miRNAs, and diseases are combined to construct the feature matrix from the biological perspectives. A novel framework based on the dual convolutional neural networks is developed to learn the global and attention representations of the lncRNA-disease associations. The left part of the framework exploits the various information contained by the feature matrix to learn the global representation of lncRNA-disease associations. The different connection relationships among the lncRNA, miRNA, and disease nodes and the different features of these nodes have the discriminative contributions for the association prediction. Hence we present the attention mechanisms from the relationship level and the feature level respectively, and the right part of the framework learns the attention representation of associations. The experimental results based on the cross validation indicate that CNNLDA yields superior performance than several state-of-the-art methods. Case studies on stomach cancer, lung cancer, and colon cancer further demonstrate CNNLDA's ability to discover the potential disease lncRNAs.

The computational prediction of novel drug-target interactions (DTIs) may hasten drug repositioning while reducing costs. Most previous methods integrated multiple kinds of connections between drugs and targets by constructing shallow models. These methods failed to learn low-dimension feature vectors for drugs and targets and ignored their distribution. We proposed a graph convolutional network and generative adversarial network-based method, GANDTI, to predict drug-target interactions. We constructed a drug-target heterogeneous network to integrate various connections between drugs and targets. A graph convolutional autoencoder was established to learn the network embedding of the drug and target nodes in a low-dimensional feature space. This encoder deeply integrated the various connections and attributes of drug and target nodes. A generative adversarial network was introduced to regularize the feature vectors into a Gaussian distribution. Severe class imbalance exists between known and unknown DTIs. We built a classifier based on the ensemble learning model, LightGBM, to evaluate the interaction propensities of drugs and targets. This classifier completely exploited all unknown DTIs and counteracted the negative effects of class imbalance. The experimental results indicated that GANDTI performs better than several state-of-the-art methods. Additionally, case studies of five drugs demonstrated the ability of GANDTI to discover potential drugs' targets.

Identification of disease-associated miRNAs (disease miRNAs) is critical for understanding disease etiology and pathogenesis. Since miRNAs exert their functions by regulating the expression of their target mRNAs, several methods based on the target genes were proposed to predict disease miRNA candidates. They achieved only limited success as they all suffered from the high false-positive rate of target prediction results. Alternatively, other prediction methods were based on the observation that miRNAs with similar functions tend to be associated with similar diseases and vice versa. The methods exploited the information about miRNAs and diseases, including the functional similarities between miRNAs, the similarities between diseases, and the associations between miRNAs and diseases. However, how to integrate the multiple kinds of information completely and consider the biological characteristic of disease miRNAs is a challenging problem. We constructed a bilayer network to represent the complex relationships among miRNAs, among diseases and between miRNAs and diseases. We proposed a non-negative matrix factorization based method to rank, so as to predict, the disease miRNA candidates. The method integrated the miRNA functional similarity, the disease similarity and the miRNA-disease associations seamlessly, which exploited the complex relationships within the bilayer network and the consensus relationship between multiple kinds of information. Considering the correlation between the candidates related to various diseases, it predicted their respective candidates for all the diseases simultaneously. In addition, the sparseness characteristic of disease miRNAs was introduced to generate more reliable prediction model that excludes those noisy candidates. The results on 15 common diseases showed a superior performance of the new method for not only well-characterized diseases but also new ones. A detailed case study on breast neoplasms, colorectal neoplasms, lung neoplasms and 32 other diseases demonstrated the ability of the method for discovering potential disease miRNAs. The web service for the new method and the list of predicted candidates for all the diseases are available at http://www.bioinfolab.top. Supplementary data are available at Bioinformatics online.

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.