Identification of Drug–Target Interactions via Dual Laplacian Regularized Least Squares with Multiple Kernel Fusion

作者：

Highlights：

• 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.

摘要

•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.

论文关键词：00-01,99-00,Drug–Target Interactions,Bipartite network,Multiple Kernel Learning,Dual Laplacian Regularized Least Squares,Graph regularized model

论文评审过程：Received 6 January 2020, Revised 8 July 2020, Accepted 10 July 2020, Available online 15 July 2020, Version of Record 16 July 2020.

论文官网地址：https://doi.org/10.1016/j.knosys.2020.106254