Turning biomedical knowledge into actionable insights
NOVADISCOVERY has developed a unique platform to scale its operations and ensure full traceability from model design to simulation outputs. This best-of-breed platform combines a community-driven “knowledge engine” to curate and organize biomedical knowledge with a modeling & simulation “action engine” designed to build, validate and apply Formal Models of pathophysiological processes to research questions.
Empowering biomedical knowledge
GITHEALTH operates as a community-driven “knowledge engine” to curate and organize biomedical knowledge extracted from white and grey literature, with the ultimate objective to build and maintain state-of-the-art Knowledge Models of pathophysiological processes (e.g. apoptosis).
The attention of biologists has focused essentially on collecting ever more data, with little regard for exploiting available knowledge. Data is heavily time- and context-dependent, and as such is not a reliable material when modeling human biology (generalizing a finding based on the analysis of a given sample is bound to generate predictions of poor quality).
Conversely, little attention has been paid to knowledge available in the scientific literature, which is doubling in quantity every decade since the 1950s. A lot of information is already out there, scattered across multiple publications in heterogeneous fields of expertise. This vast stock is unstructured, oftentimes of debatable quality and thus regrettably fails to be translated into improved therapeutic innovation and patient care capabilities.
Based on human intermediation and RDF formalism, GitHealth offers researchers and biomodelers the ability to curate, formalize and share biomedical knowledge in an open science environment.
Supporting scale and traceability
Our SIMULATION FRAMEWORK is the second pillar of our technology platform. It operates as a modeling & simulation “action engine” designed to build, validate and apply Formal Models of pathophysiological processes to research questions. These Formal Models are the mathematical and computational translations of the Knowledge Models developed in the GITHEALTH module.
The framework also supports the generation of large-scale multi-omics Virtual Population objects, as well as the handling of clinical trials data. Overall, it streamlines the application of the 30+ Standard Operating Procedures derived from the Effect Model methodology ranging from “target identification” to “clinical trials to real-world outcomes translation analyses”.