Knowledge graph-primed deep learning to identify condition-specific gene importanceKert, Aleš (Avtor) Curk, Tomaž (Mentor) Robyn Bleker, Carissa (Komentor) Zrimec, Jan (Komentor) neural networksgene expressioninteraction networksknowledge networkprior knowledgeIn 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.20242024-11-04 12:03:45Magistrsko delo/naloga164587VisID: 37125COBISS_ID: 218110723sl