Our most recent pre-print on knowledge-based deep neural networks for drug response prediction: BDKANN – Biological Domain Knowledge-based Artificial Neural Network for drug response prediction
One of the main goals of precision oncology is to predict the response of a patient to a given cancer treatment based on their genomic profile. Although current models for drug response prediction are becoming more accurate, they are also “black boxes” and cannot explain their predictions, which is of particular importance in cancer treatment. Many models also do not leverage prior biological knowledge, such as the hierarchical information on how proteins form complexes and act together in pathways. In this work, we use this prior biological knowledge to form the architecture of a deep neural network to predict cancer drug response from cell line expression data. We find that our approach not only has low prediction error compared to baseline models but also allows for meaningful interpretation of the network. These interpretations can both explain predictions made and discover novel connections in the biological knowledge that could lead to new hypotheses about mechanisms of drug action.
Read the full paper here:
Update!! BDKANN got accepted in KDD 2021! You can read the final paper here: https://dl.acm.org/doi/abs/10.1145/3447548.3467212