Prediction of Plant-Disease Relations Based on Unani Formulas by Network Analysis

Various medicinal plants are available in Bangladesh and these plants are used as traditional medicines for healing and health maintenance. Unani is one of the traditional medicine systems popular among Bangladeshi people because of its high success rate. Disease phenotype is changing constantly. It is Challenging for researchers to get the right medicinal ingredients, for the right disease, within a reasonable time. So we need to analyze the right plants for the right disease based on the existing formulas and to find out the relationship between plant and disease. The predicted plant-disease relations will help the health researcher or pharmacist for finding new drugs for new diseases. In our datasets, we have 409 plants, which are used as ingredients of 609 Unani formulas. Based on 609 formulas, we enlisted and sorted the relationship between diseases and plants. We assigned 609 Unani formulas to 18 National Center for Biotechnology Information (NCBI) disease classes. We then constructed the network of Unani formulas based on their ingredient similarity and applied DPclusO algorithm to find clusters. Clusters are associated with dominant disease and dominant plants by voting thus we established relations between plants and diseases. We predicted associations between 12 diseases and 151 plants. We validated our prediction based on the global set of Unani formulas and obtained 85.57% accuracy