Data Science

Gryphon Scientific uses data science to develop tools and techniques that extract meaning, knowledge, and insight from complex data sets.

Gryphon Scientific’s data science team is comprised of computer scientists, statisticians, and mathematicians focused on developing cutting edge techniques in machine learning and artificial intelligence. Specific areas of expertise include deep statistical analysis, natural language processing, image processing/computer vision, and deep learning. Our team is unique in that we integrate our technical expertise with a deep knowledge of the life and health sciences to ensure that technical solutions are relevant, efficient, and address client needs.

CASE STUDIES

01

Differential Diagnosis App for Tick-borne Diseases

Gryphon Scientific developed a differential diagnosis application for tick-borne diseases (TBDs) that predicts a patient’s relative likelihood of infection with different TBDs based on their symptoms, demographic risk factors, and location of likely tick exposure. This app could inform a clinical diagnosis and guide testing and treatment decisions.
02

Gene Expression Based Biomarkers of Radiation Exposure

Gryphon Scientific developed a supervised machine learning pipeline to identify the most important biomarkers (miRNA and mRNA signals) for predicting radiation exposure. This capability can help precisely determine an individual’s radiation exposure following a mass casualty nuclear event, a critical piece of data for triaging victims.
03

Scientific Community Engagement through Deep Learning

Gryphon Scientific developed a natural language processing (NLP) pipeline to process corpora of scientific literature and understand topics and potential relationships for possible collaboration. The capability used topic modeling to understand the underlying content and compared a variety of models, including deep learning, to build a highly accurate classifier. The results were used by analysts to identify scientists for possible collaboration within and across communities.

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