The NeST (Nested Systems in Tumors) map is a hierarchy of 395 protein systems that are recurrently mutated in one or more cancer types. It provides a resource of cancer mechanisms under selection for somatic mutations and captures novel protein assemblies on which mutations unexpectedly converge.
Affinity purification combined with mass spectrometry (AP-MS) is used to catalog protein-protein interactions (PPIs) for 40 proteins significantly altered in breast cancer, including multidimensional measurements across mutant and normal protein isoforms and across cancerous and noncancerous cellular contexts.
MuSIC is a hierarchical map of the eukaryotic cell architecture created from integrating immunofluorescence images in the Human Protein Atlas with affinity purification experiments from the BioPlex resource.
DrugCell is a “visible” neural network (VNN) that predicts anti-cancer drug responses by modeling the hierarchical organization of a human cancer cell. Genotypes and drug structures induce differential patterns of activity on cellular subsystems, enabling in silico investigations of the molecular mechanisms underlying cancer drug response.
DCell is a deep neural network model of budding yeast, a basic eukaryotic cell. The model structure corresponds exactly to a hierarchy of 2,526 cellular subsystems. Given this neural network structure, DCell has been trained to translate genotype to phenotype.