Na grafu znanja osnovano globoko učenje za identifikacijo pogojno-specifične pomembnosti genov

In this thesis, we sought to incorporate prior knowledge to improve the interpretability of deep learning models trained on gene expression data. We created a pipeline consisting of batch effect removal using a deep learning approach, training the prediction model, and the interpretation of the model using guided backpropagation. The prediction model architecture was con- structed using prior knowledge on molecular interactions. In genes relevant to tissue types and perturbation groups, the baseline achieved an AUC score of 0.629 and 0.597, respectively. The proposed CKN-based models achieved 18.6% and 23.1% relative improvement, with AUC scores of 0.746 and 0.735, respectively.