… By assigning more weights to the minority class, the model can be forced to pay more
attention to such classes, for effective detection. Furthermore, we introduce an explainable …

… drugs were investigated for this study, as they capture the drugs of interest (rofecoxib and
celecoxib) and drugs … We disregard uncommon drugs with less than 10,000 total dispensing …

… Knowledge graphs can be used in XAI for explainability by structuring information, … for
explainability to detect healthcare misinformation, adverse drug reactions, drug-drug interactions …

… patients’ health and drug labeling [6]. Four hundred sixty-two medicinal products were
withdrawn from the market due to adverse drug reactions in DFIs and drug-drug interactions (DDIs…

… We believe iDPath can bring revelation to the explainable deep learning technologies to
drug discovery. As a deep learning approach, iDPath is limited to in silico study, which can be …

… Conditional Random Fields to allow the calculation of the probability of drug reactions
given a input drug and a knowledge graph of drugs, proteins, pathways and phenotypes [17]. …

… There is a demand for ‘explainable’ deep learning methods to … algorithmic concepts of
explainable artificial intelligence, and … acceptance of explainable artificial intelligence techniques. …

… PhLeGrA is an analytic method implementing Hidden Conditional Random Fields to allow
the calculation of the probability of drug reactions given a input drug and a knowledge graph …

… In conclusion, the tumor-biomarker knowledge-graph based approach is an explainable
method for potential ADRs discovery based on biomarkers and might be valuable to the …

In silico models based on Deep Neural Networks (DNNs) are promising for predicting
activities and properties of new molecules. Unfortunately, their inherent black-box character …