Introducing DeepNEU
DeepNEU is our big data solution specifically engineered to empower rare diseases researchers.
This highly novel deep learning computational platform combines (1) extensive and publicly available rare diseases gene network data with (2) modified Neutrosophic Logic, and (3) features of deep ANNs and Support Vector Machines.
The current version (v2.1) of DeepNEU continues to undergo testing and has been used to generate beta versions of several rare disease models for which the underlying genetics and clinical features are reasonably well characterized. A few examples of such well characterized diseases are Cystic Fibrosis, Duchenne Muscular Dystrophy and Sickle Cell Disease. These system biology models are easily customized and in the coming weeks we will be reaching out to key opinion leaders and rare disease researchers. As part of the ongoing development and validation of this new technology we intend to make these models available free of charge to select not-for-profit researchers.
The purpose of these advanced models is to empower rare disease researchers to: (1) develop new disease hypotheses; (2) design and run experimental simulations to test those hypotheses; (3) write better grants and (4) identify potential disease specific biomarkers. |
Neutrosophic logic was originally created by Florentin Smarandache in 1995 and is an extension/combination of fuzzy logic, intuitionistic logic, paraconsistent logic, and the three-valued logics that include an indeterminate value. In the original neutrosophic logic (NL), every logical variable X is described by an ordered triple, X = (T, I, F) where T is the degree of truth, F is the degree of false and I is the degree of indeterminacy. In DeepNEU logic (dNL), X can be any relationship, degrees become probabilities and we add a new component to X that includes the probability that a relationship does not actually exist. Restated dNL(X) = (p(T(x)), p(I(x)), p(F(x)), p(0(x))) and all four probabilities are estimated by DeepNEU models. |
While DeepNEU itself is not a drug discovery engine, the identification of novel biomarkers should drive future research towards new and effective therapies where none exist now.