Predicting drug-disease interactions by semi-supervised graph cut algorithm and three-layer data integration.

ABSTRACT: BACKGROUND:Prediction of drug-disease interactions is promising for either drug repositioning or disease treatment fields. The discovery of novel drug-disease interactions, on one hand can help to find novel indictions for the approved drugs; on the other hand can provide new therapeutic approaches for the diseases. Recently, computational methods for finding drug-disease interactions have attracted lots of attention because of their far more higher efficiency and lower cost than the traditional wet experiment methods. However, they still face several challenges, such as the organization of the heterogeneous data, the performance of the model, and so on. METHODS:In this work, we present to hierarchically integrate the heterogeneous data into three layers. The drug-drug and disease-disease

SUBMITTER: Wu G 

PROVIDER: S-EPMC5751445 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature