# First person – Joshua Hack

## TL;DR

This paper uses machine learning to identify distinct disease subgroups and improve diagnosis and prognosis for individuals with SCN8A genetic variants.

## Contribution

The novel use of machine learning models to uncover distinct subgroups and enhance diagnostic accuracy in SCN8A-related diseases.

## Key findings

- Machine learning models identified distinct subgroups among individuals with SCN8A gain-of-function variants.
- The models improved diagnostic and prognostic accuracy for these individuals.
- Findings could lead to better individualized treatment strategies for rare genetic diseases.

## Abstract

First Person is a series of interviews with the first authors of a selection of papers published in Biology Open, helping researchers promote themselves alongside their papers. Joshua Hack is first author on ‘
Machine learning models reveal distinct disease subgroups and improve diagnostic and prognostic accuracy for individuals with pathogenic SCN8A gain-of-function variants’, published in BiO. Joshua is a Data Scientist in the lab of Dr Michael Hammer at the University of Arizona, investigating the mechanisms of diseases and expanding the phenotypic landscapes of rare genetic diseases towards the goal of improving treatment strategies on an individual basis.