Mining potential drug-disease associations can speed up drug repositioning for pharmaceutical companies. Previous computational strategies focused on prior biological information for association inference. However, such information may not be comprehensively available and may contain errors. Different from previous research, two inference methods, ProbS and HeatS, were introduced in this paper to predict direct drug-disease associations based only on the basic network topology measure. Bipartite network topology was used to prioritize the potentially indicated diseases for a drug. Experimental results showed that both methods can receive reliable prediction performance and achieve AUC values of 0.9192 and 0.9079, respectively. Case studies on real drugs indicated that some of the strongly predicted associations were confirmed by results in the Comparative Toxicogenomics Database (CTD). Finally, a comprehensive prediction of drug-disease associations enables us to suggest many new drug indications for further studies.
Purpose
Volumetric medical image registration has important clinical significance. Traditional registration methods may be time‐consuming when processing large volumetric data due to their iterative optimizations. In contrast, existing deep learning‐based networks can obtain the registration quickly. However, most of them require independent rigid alignment before deformable registration; these two steps are often performed separately and cannot be end‐to‐end.
Methods
We propose an end‐to‐end joint affine and deformable network for three‐dimensional (3D) medical image registration. The proposed network combines two deformation methods; the first one is for obtaining affine alignment and the second one is a deformable subnetwork for achieving the nonrigid registration. The parameters of the two subnetworks are shared. The global and local similarity measures are used as loss functions for the two subnetworks, respectively. Moreover, an anatomical similarity loss is devised to weakly supervise the training of the whole registration network. Finally, the trained network can perform deformable registration in one forward pass.
Results
The efficacy of our network was extensively evaluated on three public brain MRI datasets including Mindboggle101, LPBA40, and IXI. Experimental results demonstrate our network consistently outperformed several state‐of‐the‐art methods with respect to the metrics of Dice index (DSC), Hausdorff distance (HD), and average symmetric surface distance (ASSD).
Conclusions
The proposed network provides accurate and robust volumetric registration without any pre‐alignment requirement, which facilitates the end‐to‐end deformable registration.
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