Drug-target prediction utilizing heterogeneous bio-linked network embeddings.

To enable modularization for network-based prediction, we conducted a review of known methods conducting the various subtasks corresponding to the creation of a drug-target prediction framework and associated benchmarking to determine the highest-performing approaches. Accordingly, our contributions are as follows: (i) from a network perspective, we benchmarked the association-mining performance of 32 distinct subnetwork permutations, arranging based on a comprehensive heterogeneous biomedical network derived from 12 repositories; (ii) from a methodological perspective, we identified the best prediction strategy based on a review of combinations of the components with off-the-shelf classification, inference methods and graph embedding methods. Our benchmarking strategy consisted of two series of experiments, totaling six distinct tasks from the two perspectives, to determine the best prediction. We demonstrated that the proposed method outperformed the existing network-based methods as well as how combinatorial networks and methodologies can influence the prediction. In addition, we conducted disease-specific prediction tasks for 20 distinct diseases and showed the reliability of the strategy in predicting 75 novel drug-target associations as shown by a validation utilizing DrugBank 5.1.0. In particular, we revealed a connection of the network topology with the biological explanations for predicting the diseases, 'Asthma' 'Hypertension', and 'Dementia'. The results of our benchmarking produced knowledge on a network-based prediction framework with the modularization of the feature selection and association prediction, which can be easily adapted and extended to other feature sources or machine learning algorithms as well as a performed baseline to comprehensively evaluate the utility of incorporating varying data sources.

Tripartite Network-Based Repurposing Method Using Deep Learning to Compute Similarities for Drug-Target Prediction.

The drug discovery process is conventionally regarded as resource intensive and complex. Therefore, research effort has been put into a process called drug repositioning with the use of computational methods. Similarity-based methods are common in predicting drug-target association or the interaction between drugs and targets based on various features the drugs and targets have. Heterogeneous network topology involving many biomedical entities interactions has yet to be used in drug-target association. Deep learning can disclose features of vertices in a large network, which can be incorporated with heterogeneous network topology in order to assist similarity-based solutions to provide more flexibility for drug-target prediction. Here we describe a similarity-based drug-target prediction method that utilizes a topology-based similarity measure and two inference methods based on the similarities. We used DeepWalk, a deep learning method, to calculate the vertex similarities based on Linked Tripartite Network (LTN), which is a heterogeneous network created from different biomedical-linked datasets. The similarities are further used to feed to the inference methods, drug-based similarity inference (DBSI) and target-based similarity inference (TBSI), to obtain the predicted drug-target associations. Our previous experiments have shown that by utilizing deep learning and heterogeneous network topology, the proposed method can provide more promising results than current topology-based similarity computation methods.