Fig. 4

Schematic of a comprehensive biomedical knowledge network that supports a new taxonomy of disease. The knowledge network of disease would incorporate multiple parameters rooted in the intrinsic biology and clinical patient data originating from observational studies during normal clinical care feeding into Information Commons which are further linked to various molecular profiling data enabling the formation of a biomedical information network resulting in a new taxonomy of disease. Information Commons contains current disease information linked to individual patients and is continuously updated by a wide set of new data emerging though observational clinical studies during the course of normal health care. The data in the Information Commons and Knowledge Network provide the basis to generate a dynamic, adaptive system that informs taxonomic classification of disease. This data may also lead to novel clinical approaches such as diagnostics, treatments, prognostics, and further provide a resource for new hypotheses and basic discovery. At this intersection, artificial intelligence and machine learning may help to analyze this highly complex large dataset by pattern recognition, feature extraction yielding Digital BMs. Validation of the findings that emerge from the Knowledge Network, such as those which define new diseases or subtypes of diseases that are clinically relevant (e.g. which have implications for patient prognosis or therapy) can then be incorporated into the New Taxonomy of disease to improve diagnosis (i.e. disease classification) and treatment. This multi-parametric taxonomic classification of a disease may enable better clinical decision-making by more precisely defining a disease