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Targeted drugs have been applied to the treatment of cancer on a large scale, and some patients have certain therapeutic effects. It is a time-consuming task to detect drug–target interactions (DTIs) through biochemical experiments. At present, machine learning (ML) has been widely applied in large-scale drug screening. However, there are few methods for multiple information fusion. We propose a multiple kernel-based triple collaborative matrix factorization (MK-TCMF) method to predict DTIs. The multiple kernel matrices (contain chemical, biological and clinical information) are integrated via multi-kernel learning (MKL) algorithm. And the original adjacency matrix of DTIs could be decomposed into three matrices, including the latent feature matrix of the drug space, latent feature matrix of the target space and the bi-projection matrix (used to join the two feature spaces). To obtain better prediction performance, MKL algorithm can regulate the weight of each kernel matrix according to the prediction error. The weights of drug side-effects and target sequence are the highest. Compared with other computational methods, our model has better performance on four test data sets. [ABSTRACT FROM AUTHOR]

Diseases are usually caused by body's own defects protein or the functional structure of viral proteins. Effective drugs can be combined with these proteins well and remove original functions to achieve the therapeutic effect. The biochemical approaches of drug-target interactions (DTIs) determination is expensive and time-consuming. Therefnal-based methods have been proposed to predict new DTIs. In order to solve the problem of multiple information fusion, we propose a multi-view graph regularized link propagation model (MvGRLP) to predict new DTIs. Multi-view learning could use the complementary and correlated information between different views (features). Compared with existing models, our method achieves comparable and best results on four benchmark datasets. [ABSTRACT FROM AUTHOR]

With the emergence of large-scale experimental data on genes and proteins, drug discovery and repositioning will be more difficult in the field of biomedical research. More and more resources are needed for detecting drug–target interactions (DTIs) in the experimental works. The interactions between drugs and targets could been seen as a bipartite network. Many computational methods have been developed to identify DTIs. However, most of them did not integrate multiple information and filter noise or outlier points. In this paper, we develop a fuzzy bipartite local model (FBLM) based on fuzzy least squares support vector machine and multiple kernel learning (MKL) for predicting DTIs. First, multiple kernels are constructed in drug and target spaces, respectively. Then, all corresponding kernels are combined by MKL algorithm in two spaces. Finally, FBLM is employed to identify DTIs. Our proposed approach is tested on four benchmark datasets under three types of cross validation. Comparing with existing outstanding methods, our method is a useful tool for the DTIs prediction. [ABSTRACT FROM AUTHOR]

Detection of Drug–Target Interactions (DTIs) is the time-consuming and laborious experiment via biochemical approaches. Machine learning based methods have been widely used to mine meaningful information of drug research. In this study, we establish a novel computational method to predict DTIs via Dual Laplacian Regularized Least Squares model (DLapRLS) with Hilbert–Schmidt Independence Criterion-based Multiple Kernel Learning (HSIC-MKL). Multiple kernels are built from different information sources (drug and target spaces). Then, above corresponding kernels are integrated by HSIC-MKL. At last, DLapRLS model is trained by Alternating Least Squares Algorithm (ALSA) and employed to predict new DTIs. On four benchmark datasets, the results of our method are comparable and even better than existing models. • DLapRLS employs alternating least squares algorithm to solve the final model. • Heterogeneous information (kernels) is integrated via multiple kernel learning. • For HSIC-MKL, we employ the Laplacian regular term to smooth the weights. [ABSTRACT FROM AUTHOR]

Background: Identifying potential associations between genes and diseases via biomedical experiments must be the time-consuming and expensive research works. The computational technologies based on machine learning models have been widely utilized to explore genetic information related to complex diseases. Importantly, the gene-disease association detection can be defined as the link prediction problem in bipartite network. However, many existing methods do not utilize multiple sources of biological information; Additionally, they do not extract higher-order relationships among genes and diseases. Results: In this study, we propose a novel method called Dual Hypergraph Regularized Least Squares (DHRLS) with Centered Kernel Alignment-based Multiple Kernel Learning (CKA-MKL), in order to detect all potential gene-disease associations. First, we construct multiple kernels based on various biological data sources in gene and disease spaces respectively. After that, we use CAK-MKL to obtain the optimal kernels in the two spaces respectively. To specific, hypergraph can be employed to establish higher-order relationships. Finally, our DHRLS model is solved by the Alternating Least squares algorithm (ALSA), for predicting gene-disease associations. Conclusion: Comparing with many outstanding prediction tools, DHRLS achieves best performance on gene-disease associations network under two types of cross validation. To verify robustness, our proposed approach has excellent prediction performance on six real-world networks. Our research work can effectively discover potential disease-associated genes and provide guidance for the follow-up verification methods of complex diseases. [ABSTRACT FROM AUTHOR]

Identification of associations between non-coding RNAs and diseases plays an important role in the study of pathogenesis, which has been a hot topic in recent research. However, traditional methods are time-consuming to detect the associations between non-coding RNAs and diseases. Recently, associations of non-coding RNAs and diseases can be regarded as bipartite network. In this paper, we propose a novel deep multiple kernel learning method, called the multi-layer multi-kernel deep neural network (MLMKDNN). First, many feature matrices are built by multiple features of non-coding RNAs and diseases. Then, these feature matrices are mapped into kernel space and fused by deep neural network. Finally, combine two fused output of MLMKDNN as the predicted values. Three types of non-coding RNAs (miRNA, circRNA and lncRNA) are used to test the performance of MLMKDNN. Compared with other existing methods, our proposed model has high Area Under Precision Recall (AUPR) value on three types of datasets. Experimental results confirm that our method is an effective predictive tool. It provides a framework that can also be applied to the link prediction of other bipartite networks. [ABSTRACT FROM AUTHOR]

Complex and diverse microbial communities have certain impacts on human health, and specific drugs are needed to treat diseases caused by microbes. However, most of the discovery of associations between microbes and drugs is through biological experiments, which are time-consuming and expensive. Therefore, it is crucial to develop an effective and computational model to detect novel microbe–drug associations. In this study, we propose a model based on Multiple Kernel fusion on Graph Convolutional Network, called MKGCN, for inferring novel microbe–drug associations. Our model is built on the heterogeneous network of microbes and drugs to extract multi-layer features, through Graph Convolutional Network (GCN). Then, we respectively calculate the kernel matrix by embedding features on each layer, and fuse multiple kernel matrices based on the average weighting method. Finally, Dual Laplacian Regularized Least Squares is used to infer new microbe–drug associations by the combined kernel in microbe and drug spaces. Compared with the existing tools for detecting biological bipartite networks, our model has excellent prediction effect on three datasets via three types of cross-validation. Furthermore, we also conduct a case study of the SARS-Cov-2 virus and make a deduction about drugs that may be able to associate with COVID-19. We have proved the accuracy of the prediction results through the existing literature. [Display omitted] • Our model applies the Graph Convolutional Network into Multiple Kernel fusion. • We apply multi-layer GCN to extract different structural information in the graph. • Our method has excellent performance on three microbe–drug association datasets. • We discover some potential drugs being associated with COVID-19. [ABSTRACT FROM AUTHOR]

Flow diagram of hypergraph regularized bipartite local model (HGBLM). • HGBLM approach builds more complex relationships between samples by hypergraph. • Heterogeneous information (similarity matrices) is integrated via CKA-MKL. • HGBLM is built via hypergraph embedded LapSVM to further improve the performance of prediction. MicroRNA (miRNA) plays an important role in life processes. In recent years, predicting the association between miRNAs and diseases has become a research hotspot. However, biological experiments take a lot of time and cost to identify pathogenic miRNAs. Computational biology-based methods can effectively improve accuracy of recognition. In our study, miRNAs-disease associations are predicted by a hypergraph regularized bipartite local model (HGBLM), which is based on hypergraph embedded Laplacian support vector machine (LapSVM). On benchmark dataset, the results of our method are comparable and even better than existing models. [ABSTRACT FROM AUTHOR]