Herb-disease association prediction model based on network consistency projection

A growing number of biological and clinical reports indicate the usefulness of herbs in the treatment of complex human diseases, giving an essential supplement for modern medicine. Similar to drugs, the use of experimental validation to identify related diseases of given herbs is both expensive and time-consuming. Such validation is even more difficult because each herb always contains several components. It is alternative to design computational models to predict herb-disease associations (HDAs). Nevertheless, only a few computational models have been developed for HDA prediction. In this study, we make full use of several properties of herbs and diseases, which are collected in a public database HERB, to design a model named HDAPM-NCP for predicting HDAs. Based on these properties, six herb kernels and five disease kernels are constructed, which are further fused into one unified herb kernel and one disease kernel. These kernels and herb-disease adjacency matrix are fed into network consistency projection to quantify the strength of herb-disease pairs. The cross-validation results show the high performance of HDAPM-NCP. Such performance is higher than that of two previous models. The ablation experiments prove the effects of modules in this model. Finally, we also analyze the weakness and strength of the model, uncovering which herb-disease pairs that HDAPM-NCP can yield reliable or unsatisfied predictions, and a case study is conducted to prove that HDAPM-NCP can discover latent HDAs.