  Diseases involve complex processes and modifications to the cellular
machinery. The gene expression profile of the affected cells contains
characteristic patterns linked to a disease. Hence, biological knowledge
pertaining to a disease can be derived from a patient cell's profile, improving
our diagnosis ability, as well as our grasp of disease risks. This knowledge
can be used for drug re-purposing, or by physicians to evaluate a patient's
condition and co-morbidity risk. Here, we look at differential gene expression
obtained from microarray technology for patients diagnosed with various
diseases. Based on this data and cellular multi-scale organization, we aim to
uncover disease--disease links, as well as disease-gene and disease--pathways
associations. We propose neural networks with structures inspired by the
multi-scale organization of a cell. We show that these models are able to
correctly predict the diagnosis for the majority of the patients. Through the
analysis of the trained models, we predict and validate disease-disease,
disease-pathway, and disease-gene associations with comparisons to known
interactions and literature search, proposing putative explanations for the
novel predictions that come from our study.
