Biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data. (arXiv:2207.00812v1 [q-bio.QM])

In this paper we provide a structured literature analysis focused on Deep
Learning (DL) models used to support inference in cancer biology with a
particular emphasis on multi-omics analysis. The work focuses on how existing
models address the need for better dialogue with prior knowledge, biological
plausibility and interpretability, fundamental properties in the biomedical
domain. We discuss the recent evolutionary arch of DL models in the direction
of integrating prior biological relational and network knowledge to support
better generalisation (e.g. …

arxiv bio cancer data deep learning encoding learning oncology trends