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Graphable Receives National Science Foundation Grant with Goal of Early Infectious Disease Detection

·4 min read

BOSTON, Nov. 17, 2020 /PRNewswire/ -- Graphable, the world's leading Graph-based Data Science / AppDev / Analytics consultancy, Neo4j reseller, and exclusive reseller of the Hume platform in the Americas announced today, that along with the University of South Florida (USF), the organization applied for and has received a National Science Foundation (NSF) grant focused on the utilization of advanced mathematical concepts and their application in predictive analysis and in identifying patterns and trends present when faced with early disease detection and prevention initiatives. Specifically, Graphable looks to study ways to detect future large-scale pandemics, like COVID-19, much earlier.

Self-organizing systems - Lorenz Attractor
Self-organizing systems - Lorenz Attractor

NSF grant for using network science in early detection of highly infectious diseases, prior to pandemic outbreak.

Through a public-private partnership with the University of South Florida, Graphable seeks to introduce a new method of analyzing the trajectory of a pandemic via the use of research information that is centered on the idea of self-organizing systems and seemingly obscure, but detectable, patterns. The organization believes that this approach is efficacious when compared to tracking the spread of disease through contact tracing and other reactionary methods after a virus has already taken hold. The opportunity to predict an impending situation through early detection of the virus should enable scientists and medical researchers to act in time to avoid uncontrolled outbreaks.

"We have seen firsthand what has occurred with COVID-19 and how the late response and reactionary, haphazard approach to preventing full-scale outbreak has been ineffective," notes Graphable CEO Kyle McNamara. "A scientific approach is needed for early detection of highly infectious diseases prior to them transforming into pandemics, instead of attempting stop-gap measures after the disease has reached pandemic proportions."

The approach is the first to marry the theory of self-organizing systems with network science. And it also leverages pioneering Natural Language Processing (NLP) and Machine Learning (ML) on graphs.

"The Graphable team has proven to be a transformative partner in the NSF CNS Research Grant #2028051 effort to use novel approaches to detect infectious disease occurrences much earlier. Graphable has brought emerging technology solutions and research methods of knowledge graphs, NLP and ML that have enabled us to analyze research data in ways that weren't available to us in the past. Graphable's contributions will allow interdisciplinary researchers to work across a scientific and engineering ecosystem using global Indicators of how underlying pandemic patterns and distribution of outbreaks are formed," noted Dr. Sylvia Thomas, Associate Professor of Electrical Engineering at the University of South Florida.

Ultimately, it is through the blending of structured and unstructured text in a knowledge graph using advanced NLP / ML techniques that it is hypothesized that hidden connections and patterns between diseases, drugs, and other pertinent topics will be uncovered.

Another goal is to add time as a critical dimension in finding the trajectory of growth in connected terms and reaching intellectual convergence and divergence. The study is expected to be completed over the course of one year.

Dr. Pamela McCauley, former NSF I-Corps Program Director and current Associate Dean of Academic Programs at North Carolina State University, stated, "We were really excited to fund this project at NSF, particularly since it brought a cutting-edge approach to the critical problem of early detection of infectious disease across the globe, using network science and graph databases. Tracking the way dots are connected over time from scholarly research articles using knowledge graphs to detect the path of the pandemic is a novel concept and much needed application of innovative scientific knowledge."

In the long term the Graphable team seeks to implement the solution as an open source database that can be accessed worldwide while simultaneously helping government agencies at home and abroad, such as the CDC and the National Institute of Allergy and Infectious Diseases (NIAID). The company wants the scientific community to have access to an early warning detection system that can minimize the risk of future pandemics as well as reduce the impact events like this can have on public health, global economic stability, and the lives of everyday people.

About Graphable, Inc.

Aside from being the Americas exclusive reseller of the cutting-edge Hume NLP / ML / Knowledge Graph platform, Graphable is also the world's leading Graph Data Science / AppDev / Analytics consultancy and a Neo4j reseller.

Graphable offers expertise at all stages for projects that use or are considering using Graph Data Science / Analytics, Neo4j, NLP / ML, AI, Elasticsearch and more, including graph model validation, architecture, implementation, performance tuning, training, and beyond.

Contacts:
Graphable, Inc. PR
+1 844-472-7471, x2
pr@graphable.ai

https://www.graphable.ai

Graphable delivers insightful graphdb / ml / nlp as well as traditional analytics with measurable impact.
Graphable delivers insightful graphdb / ml / nlp as well as traditional analytics with measurable impact.
Cision
Cision

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SOURCE Graphable, Inc.