The Chemical Effect Predictor (CEP) predictions for conazoles

The Chemical Effect Predictor (CEP) is a machine learning predictive tool based on data extracted from a Knowledge Graph (KG) designed for predicting hazard of drugs/chemicals in human health. The KG represents the complexity of human biology and how the action of the compound can affect human health through the intricate network of proteins and their associated biological processes to the disease phenotype.

Based on this KG representing biological relationships, different variables describing the association between drug/chemical and diseases are extracted for training a machine learning predictive model based on a random forest algorithm. The machine learning model trained on external data reporting adverse effects caused by drugs is used for predicting hazards for query drugs/chemical of our interest. Each drug/chemical-disease association is assigned a prediction value ranging from 0 to 1, with 1 indicating the highest predicted probability that the query compound will cause the specific adverse effect.

To support the analysis of CEP results, and identify robust signals for the prioritization of specific health effect concerns, the Disease Set Enrichment Analysis (DSEA) approach was designed. DSEA uses the classification of diseases in higher-order System Organ Classes (SOCs) and the ranked list of predicted diseases based on the prediction value and calculates an Normalized Enrichment score (NES) and  adjusted p-value for each SOC. As a result, the hazard prediction for each compound is represented by 22  SOCs with its NES and adjusted p-value, which can be used to identify disease areas of concern for the compound.