Semi-supervised learning approach to inter-species translation.
Semi-supervised learning begins by training an initial supervised model on the mouse data alone and applying the model to a human test data. Human samples with the highest prediction confidence are used to create an augmented training dataset of mouse and human samples with predicted phenotypes. A new model is trained on this augmented training set and applied to reclassify the human samples. Predictions are finalized when all human samples are merged with the training set. Predicted human differentially expressed genes and enriched pathways are validated against genes and pathways identified using the true human phenotypes.