Deep Neural Networks (DNNs) are renowned for their high accuracy and versatility, which has led to their application in many fields of research, including biology. However, this accuracy often comes at the expense of interpretability, making it challenging to reason about the inner workings of most DNNs. Particularly in biological research, understanding the mechanisms behind specific outcomes is highly valuable. To elucidate the latent space of DNNs in the context of cancer biology, we introduce GONNECT: a Gene Ontology-derived Neural Network for Explainable Cancer Typing. GONNECT incorporates biological prior knowledge from the Gene Ontology (GO) directly into its network architecture, enabling interpretability through model structure. Using an autoencoder framework, we evaluate GONNECT as both encoder and decoder module and demonstrate its ability to learn which biological processes are distinctive for different cancer types. Furthermore, we show how a variant including soft links (GONNECT-SL) can expand on current knowledge by proposing new interactions between biological processes. GONNECT is flexible both in the amount of prior knowledge it incorporates and the set of input genes.
OptoLoop: An optogenetic tool to probe the functional role of genome organization
The genome folds inside the cell nucleus into hierarchical architectural features, such as chromatin loops and domains. If and how this genome organization influences the