In this paper, we propose a model, called the Multiple Kernel-based Dual Graph Regularized Least Squares Model (MKDGRLS), to predict potential drug–disease ...
In this paper, we propose a model, called the Multiple Kernel-based Dual Graph Regularized Least Squares Model (MKDGRLS), to predict potential drug-disease ...
Dec 5, 2023 · In this article, we propose a heterogeneous graph embedding method called HMLKGAT to infer novel potential drugs for diseases. More specifically ...
Prediction of Drug–Disease Associations Based on Multi-Kernel ...
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In this paper, we propose a heterogeneous graph embedding method called HMLKGAT to infer novel potential drugs for diseases. More specifically, we first ...
Aug 9, 2021 · In this study, we propose a novel method called Dual Hypergraph Regularized Least Squares (DHRLS) with Centered Kernel Alignment-based Multiple ...
In this article, we propose a heterogeneous graph embedding method called HMLKGAT to infer novel potential drugs for diseases. More specifically, we first ...
Improved prediction of drug-target interactions using regularized least squares integrating with kernel fusion technique. · A multiple kernel learning algorithm ...
In drug discovery, the foremost challenging task is predicting drug-disease correlation using drugs' various indications and side effects on specific diseases ...
Jul 5, 2023 · In this paper, we propose a novel framework, manifold optimization based kernel preserving embedding (MOKPE), to efficiently solve the problem of modeling ...
Apr 3, 2020 · The algorithm is specially designed for binary data and uses parallel proximal algorithm to solve the aforesaid minimization problem taking into ...