Applying data science methodologies with artificial intelligence variant reinterpretation to map and estimate genetic disorder prevalence utilizing clinical data.

Suellen Jackson¹, Rebecca Freeman^1,2, Adriana Noronha¹, Hafsah Jamil¹, Eric Chavez¹, Jason Carmichael¹, Kaylee M Ruiz¹, Christine Miller¹, Sarah Benke¹, Rosalie Perrot¹, Maryam Hockley¹, Kady Murphy¹, Aimiel Casillan¹, Lily Radanovich¹, Roger Deforest¹, Mark E Nunes¹, Carolina Galarreta-Aima¹, Richard Sidlow¹, Yaron Einhorn³, Jeremy Woods^1,4,5,6

¹Valley Children's Hospital, Madera, California, USA.

²UCSF Benioff Children's Hospital Oakland, Oakland, California, USA.

³Genoox, Tel Aviv, Israel.

⁴Stanford University, Palo Alto, California, USA.

⁵Eureka Institute for Translational Medicine, Siracusa, Italy.

⁶Translation Science Foundation, Fresno, California, USA.

PubMed DOI

Abstract

Data science methodologies can be utilized to ascertain and analyze clinical genetic data that is often unstructured and rarely used outside of patient encounters. Genetic variants from all genetic testing resulting to a large pediatric healthcare system for a 5-year period were obtained and reinterpreted utilizing the previously validated Franklin© Artificial Intelligence (AI). Using PowerBI©, th…

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