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Poster: 55th Austrian Society for Gastroenterology and Hepatology (ÖGGH) 2022

By September 9, 2022October 12th, 2022No Comments

BiomeDx presents newest findings on microbiome signatures predicting colorectal cancer and adenoma.

VIENNA, September 8th: At the 55th Austrian Society for Gastroenterology and Hepatology (https://aerztezeitung.at/event/55-jahrestagung-32-fortbildungskurs-der-oeggh/), the BiomeDx team presented their newest results from a machine learning analysis of colorectal cancer and adenoma microbiome signatures for the application of a potential screening tool. The findings were presented in the form of a poster and discussed with academic microbiome experts and pharmaceutical representatives alike.

I was even more proud of our project when I heard the compliments of medical experts in the field of gastroenterology! They were impressed with the performance of our models compared to current non-invasive tests on the market. It was a fun and educational experience to attend this congress!

Katarina Priselac from BiomeDx

About BiomeCRC®

BiomeCRC is a colorectal cancer preventive screening test based on the intestinal microbiome. The tool is powered by the proven BiomeDx platform and data from numerous clinical trials. Ongoing research in collaboration with the Medical University of Vienna includes over 1000 probands. Read more on BiomeCRC.

About BiomeDx®

Biome Diagnostics GmbH (BiomeDx) is world leader in microbiome diagnostic products for oncology. The Austrian based MedTech company is committed to advance precision medicine by pioneering microbiome-based technologies that transform cancer care. The company is strategically positioned at the intersection of state-of-the-art DNA sequencing and advanced machine learning algorithms to develop first in class technologies for routine clinical practice. It is the world first microbiome company certified according to ISO 13485:2016 and ISO 9001:2015.

Presented poster: Analysis of colorectal cancer and adenoma microbiome signatures and the application of machine learning classification as a potential screening tool

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