Ping Zhang, Weidan Cao, et al.
BMC Bioinformatics
Drug co-prescription (or drug combination) is a therapeutic strategy widely used as it may improve efficacy and reduce side-effect (SE). Since it is impractical to screen all possible drug combinations for every indication, computational methods have been developed to predict new combinations. In this study, we describe a novel approach that utilizes clinical SEs from post-marketing surveillance and the drug label to predict 1,508 novel drug-drug combinations. It outperforms other prediction methods, achieving an AUC of 0.92 compared to an AUC of 0.69 in a previous method, on a much larger drug combination set (245 drug combinations in our dataset compared to 75 in previous work.). We further found from the feature selection that three FDA black-box warned serious SEs, namely pneumonia, haemorrhage rectum, and retinal bleeding, contributed mostly to the predictions and a model only using these three SEs can achieve an average area under curve (AUC) at 0.80 and accuracy at 0.91, potentially with its simplicity being recognized as a practical rule-of-three in drug co-prescription or making fixed-dose drug combination. We also demonstrate this performance is less likely to be influenced by confounding factors such as biased disease indications or chemical structures.
Ping Zhang, Weidan Cao, et al.
BMC Bioinformatics
Heng Luo, Ping Zhang, et al.
Scientific Reports
Janu Verma, Heng Luo, et al.
PacificVis 2017
Ping Zhang, Fei Wang, et al.
AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium