The computational prediction of drug-disease interactions using the dual-network L_2,1-CMF method.

ABSTRACT:

Background

Predicting drug-disease interactions (DDIs) is time-consuming and expensive. Improving the accuracy of prediction results is necessary, and it is crucial to develop a novel computing technology to predict new DDIs. The existing methods mostly use the construction of heterogeneous networks to predict new DDIs. However, the number of known interacting drug-disease pairs is small, so there will be many errors in this heterogeneous network that will interfere with the final results.

Results

A novel method, known as the dual-network L_2,1-collaborative matrix factorization, is proposed to predict novel DDIs. The Gaussian interaction profile kernels and L_2,1-norm are introduced in our method to achieve better results than other advanced methods. The

SUBMITTER: Cui Z 

PROVIDER: S-EPMC6320570 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature