Learning Representation of Molecules in Association Network for Predicting Intermolecular Associations

Drug-disease association prediction using semantic graph and function similarity representation learning over heterogeneous information networks

Discovering new indications for existing drugs is a promising development strategy at various stages of drug research and development. However, most of them complete their tasks by constructing a variety of heterogeneous networks without considering available higher-order connectivity patterns in heterogeneous biological information networks, which are believed to be useful for improving the accuracy of new drug discovering. To this end, we propose a computational-based model, called SFRLDDA, for drug-disease association prediction by using semantic graph and function similarity representation learning. Specifically, SFRLDDA first integrates a heterogeneous information network (HIN) by drug-disease, drug-protein, protein-disease associations, and their biological knowledge. Second, different representation learning strategies are applied to obtain the feature representations of drugs and diseases from different perspectives over semantic graph and function similarity graphs constructed, respectively. At last, a Random Forest classifier is incorporated by SFRLDDA to discover potential drug-disease associations (DDAs). Experimental results demonstrate that SFRLDDA yields a best performance when compared with other state-of-the-art models on three benchmark datasets. Moreover, case studies also indicate that the simultaneous consideration of semantic graph and function similarity of drugs and diseases in the HIN allows SFRLDDA to precisely predict DDAs in a more comprehensive manner.

Tuberculosis Diagnosis Using Deep Transferred EfficientNet

Tuberculosis is a very deadly disease, with more than half of all tuberculosis cases dead in countries and regions with relatively poor health care resources. Fortunately, the disease is curable, and early diagnosis and medication can go a long way toward curing TB patients. Unfortunately, traditional methods of TB diagnosis rely on specialist doctors, which is lacking in areas with high TB mortality rates. Diagnostic methods based on artificial intelligence technology are one of the solutions to this problem. We propose a Deep Transferred EfficientNet with SVM (DTE-SVM), which replaces the pre-trained EfficientNet classification layer with an SVM classifier and achieves auspicious performance on a small dataset. After ten runs of 10-fold Cross-Validation, the DTE-SVM has a sensitivity of 93.89±1.96, a specificity of 95.35±1.31, a precision of 95.30±1.24, an accuracy of 94.62±1.00, and an F1-score of 94.62±1.00. In addition, our study conducted ablation studies on the effect of the SVM classifier on model performance and briefly discussed the results.

Enhancing Drug-Drug Interaction Prediction Using Deep Attention Neural Networks

Drug-drug interactions are one of the main concerns in drug discovery. Accurate prediction of drug-drug interactions plays a key role in increasing the efficiency of drug research and safety when multiple drugs are co-prescribed. With various data sources that describe the relationships and properties between drugs, the comprehensive approach that integrates multiple data sources would be considerably effective in making high-accuracy prediction. In this paper, we propose a Deep Attention Neural Network based Drug-Drug Interaction prediction framework, abbreviated as DANN-DDI, to predict unobserved drug-drug interactions. First, we construct multiple drug feature networks and learn drug representations from these networks using the graph embedding method; then, we concatenate the learned drug embeddings and design an attention neural network to learn representations of drug-drug pairs; finally, we adopt a deep neural network to accurately predict drug-drug interactions. The experimental results demonstrate that our model DANN-DDI has improved prediction performance compared with state-of-the-art methods. Moreover, the proposed model can predict novel drug-drug interactions and drug-drug interaction-associated events.

MFIDMA: A Multiple Information Integration Model for the Prediction of Drug-miRNA Associations

Abnormal microRNA (miRNA) functions play significant roles in various pathological processes. Thus, predicting drug-miRNA associations (DMA) may hold great promise for identifying the potential targets of drugs. However, discovering the associations between drugs and miRNAs through wet experiments is time-consuming and laborious. Therefore, it is significant to develop computational prediction methods to improve the efficiency of identifying DMA on a large scale. In this paper, a multiple features integration model (MFIDMA) is proposed to predict drug-miRNA association. Specifically, we first formulated known DMA as a bipartite graph and utilized structural deep network embedding (SDNE) to learn the topological features from the graph. Second, the Word2vec algorithm was utilized to construct the attribute features of the miRNAs and drugs. Third, two kinds of features were entered into the convolution neural network (CNN) and deep neural network (DNN) to integrate features and predict potential target miRNAs for the drugs. To evaluate the MFIDMA model, it was implemented on three different datasets under a five-fold cross-validation and achieved average AUCs of 0.9407, 0.9444 and 0.8919. In addition, the MFIDMA model showed reliable results in the case studies of Verapamil and hsa-let-7c-5p, confirming that the proposed model can also predict DMA in real-world situations. The model was effective in analyzing the neighbors and topological features of the drug-miRNA network by SDNE. The experimental results indicated that the MFIDMA is an accurate and robust model for predicting potential DMA, which is significant for miRNA therapeutics research and drug discovery.

RLFDDA: a meta-path based graph representation learning model for drug-disease association prediction

BACKGROUND

Drug repositioning is a very important task that provides critical information for exploring the potential efficacy of drugs. Yet developing computational models that can effectively predict drug-disease associations (DDAs) is still a challenging task. Previous studies suggest that the accuracy of DDA prediction can be improved by integrating different types of biological features. But how to conduct an effective integration remains a challenging problem for accurately discovering new indications for approved drugs.

METHODS

In this paper, we propose a novel meta-path based graph representation learning model, namely RLFDDA, to predict potential DDAs on heterogeneous biological networks. RLFDDA first calculates drug-drug similarities and disease-disease similarities as the intrinsic biological features of drugs and diseases. A heterogeneous network is then constructed by integrating DDAs, disease-protein associations and drug-protein associations. With such a network, RLFDDA adopts a meta-path random walk model to learn the latent representations of drugs and diseases, which are concatenated to construct joint representations of drug-disease associations. As the last step, we employ the random forest classifier to predict potential DDAs with their joint representations.

RESULTS

To demonstrate the effectiveness of RLFDDA, we have conducted a series of experiments on two benchmark datasets by following a ten-fold cross-validation scheme. The results show that RLFDDA yields the best performance in terms of AUC and F1-score when compared with several state-of-the-art DDAs prediction models. We have also conducted a case study on two common diseases, i.e., paclitaxel and lung tumors, and found that 7 out of top-10 diseases and 8 out of top-10 drugs have already been validated for paclitaxel and lung tumors respectively with literature evidence. Hence, the promising performance of RLFDDA may provide a new perspective for novel DDAs discovery over heterogeneous networks.

BNEMDI: A Novel MicroRNA–Drug Interaction Prediction Model Based on Multi-Source Information With a Large-Scale Biological Network

As a novel target in pharmacy, microRNA (miRNA) can regulate gene expression under specific disease conditions to produce specific proteins. To date, many researchers leveraged miRNA to reveal drug efficacy and pathogenesis at the molecular level. As we all know that conventional wet experiments suffer from many problems, including time-consuming, labor-intensity, and high cost. Thus, there is an urgent need to develop a novel computational model to facilitate the identification of miRNA–drug interactions (MDIs). In this work, we propose a novel bipartite network embedding-based method called BNEMDI to predict MDIs. First, the Bipartite Network Embedding (BiNE) algorithm is employed to learn the topological features from the network. Then, the inherent attributes of drugs and miRNAs are expressed as attribute features by MACCS fingerprints and k-mers. Finally, we feed these features into deep neural network (DNN) for training the prediction model. To validate the prediction ability of the BNEMDI model, we apply it to five different benchmark datasets under five-fold cross-validation, and the proposed model obtained excellent AUC values of 0.9568, 0.9420, 0.8489, 0.8774, and 0.9005 in ncDR, RNAInter, SM2miR1, SM2miR2, and SM2miR MDI datasets, respectively. To further verify the prediction performance of the BNEMDI model, we compare it with some existing powerful methods. We also compare the BiNE algorithm with several different network embedding methods. Furthermore, we carry out a case study on a common drug named 5-fluorouracil. Among the top 50 miRNAs predicted by the proposed model, there were 38 verified by the experimental literature. The comprehensive experiment results demonstrated that our method is effective and robust for predicting MDIs. In the future work, we hope that the BNEMDI model can be a reliable supplement method for the development of pharmacology and miRNA therapeutics.

Graph representation learning in bioinformatics: trends, methods and applications

Graph is a natural data structure for describing complex systems, which contains a set of objects and relationships. Ubiquitous real-life biomedical problems can be modeled as graph analytics tasks. Machine learning, especially deep learning, succeeds in vast bioinformatics scenarios with data represented in Euclidean domain. However, rich relational information between biological elements is retained in the non-Euclidean biomedical graphs, which is not learning friendly to classic machine learning methods. Graph representation learning aims to embed graph into a low-dimensional space while preserving graph topology and node properties. It bridges biomedical graphs and modern machine learning methods and has recently raised widespread interest in both machine learning and bioinformatics communities. In this work, we summarize the advances of graph representation learning and its representative applications in bioinformatics. To provide a comprehensive and structured analysis and perspective, we first categorize and analyze both graph embedding methods (homogeneous graph embedding, heterogeneous graph embedding, attribute graph embedding) and graph neural networks. Furthermore, we summarize their representative applications from molecular level to genomics, pharmaceutical and healthcare systems level. Moreover, we provide open resource platforms and libraries for implementing these graph representation learning methods and discuss the challenges and opportunities of graph representation learning in bioinformatics. This work provides a comprehensive survey of emerging graph representation learning algorithms and their applications in bioinformatics. It is anticipated that it could bring valuable insights for researchers to contribute their knowledge to graph representation learning and future-oriented bioinformatics studies.

In silico drug repositioning using deep learning and comprehensive similarity measures

BACKGROUND

Drug repositioning, meanings finding new uses for existing drugs, which can accelerate the processing of new drugs research and development. Various computational methods have been presented to predict novel drug-disease associations for drug repositioning based on similarity measures among drugs and diseases. However, there are some known associations between drugs and diseases that previous studies not utilized.

METHODS

In this work, we develop a deep gated recurrent units model to predict potential drug-disease interactions using comprehensive similarity measures and Gaussian interaction profile kernel. More specifically, the similarity measure is used to exploit discriminative feature for drugs based on their chemical fingerprints. Meanwhile, the Gaussian interactions profile kernel is employed to obtain efficient feature of diseases based on known disease-disease associations. Then, a deep gated recurrent units model is developed to predict potential drug-disease interactions.

RESULTS

The performance of the proposed model is evaluated on two benchmark datasets under tenfold cross-validation. And to further verify the predictive ability, case studies for predicting new potential indications of drugs were carried out.

CONCLUSION

The experimental results proved the proposed model is a useful tool for predicting new indications for drugs or new treatments for diseases, and can accelerate drug repositioning and related drug research and discovery.