How we do itWe apply the in silico trials standard methodology with the most robust and versatile technology platform in our industry
#EffectModel #diseasemodels #virtualpatients #WISE #GitHealth #SimWork #Haskell
The WISE engine
The company’s “Whitebox In Silico Engine” combines a community- driven “knowledge engine” to curate and organize biomedical knowledge (GitHealth) with a modeling & simulation “action engine” to ensure scalable and reproducible simulation capabilities (SimWork).
The WISE ensure full traceability from a simulation output back to each primary source (scientific article, dataset) to avoid the black box syndrome and facilitate the model validation process.
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). Based on human intermediation (for the curation) and semantic web formalism (for the exploitation), GitHealth offers researchers and biomodelers the ability to curate, formalize and share biomedical knowledge extracted from scientific literature in an open science environment.
Its key features include:
- Powerful RDF reasoning backbone to uncover links between pieces of knowledge that would otherwise have eluded human attention
- Structured annotations paving the way for the development of computational models of pathophysiological systems of interest
Graphic modeling of the immune system:
Programmed in a functional language called Haskell, our simulation framework is a highly scalable analytics platform which enables simulated outcome-based reasoning and decision making. The framework also supports the generation of large-scale multi-omics virtual population objects.
Nova simulation framework is at the ideal intersection of desirable machine learning features
Overall, it industrializes the application of the 30+ M&S Standard Operating Procedures derived from the Effect Model methodology ranging from “target identification” to “clinical trials to real-world outcomes translation analyses”.
How we do it
The Effect Model enables us -according to the indicators that you have, either clinical, genetic, imaging or biomarkers, – to go down to the patient and to go much further in an individual prescription.
Our work is to really get to the bottom of these mechanisms. To identify step-by-step the sequences that lead to the disease and to the treatment that can be given.
In itself, the virtual population, irrespective of its important role in the use of models, has other qualities. The first quality is to be able to collect data that are today stored in various dispersed databases…
In this European project called SysClad, Novadiscovery was instructed to explore in-silico the
efficacy of a partial blocking of the signaling pathway mTor for the prevention of lung transplant rejection.