Max Kelsen grants residency for gene researcher to discover why patients develop severe COVID symptoms – iTWire


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Artificial intelligence and machine learning solution business Max Kelsen is providing a residency for gene researcher Professor Sudha Rao in a quest to develop an easy-to-use test to help predict why some patients will catch severe symptoms of COVID-19.

Professor Sudha Rao is based at the QIMR Berghofer Medical Research Institute in Brisbane.

She is investigating to isolate epigenetic factors which may contribute to individuals having severe symptoms of COVID-19.

Despite the scientific community’s knowledge about the disease, the variability of health impacts on individuals remains a significant challenge due to the complexity of the potential causal factors, including at the DNA level.

“COVID-19 infection causes changes at the DNA level of cells, creating an early footprint that offers important clues as to whether an individual is going to get severe disease or long COVID-19. With my expertise in clinical cutting-edge epigenetic biology and Max Kelsen's AI expertise, we have an amazing opportunity to rapidly unravel the molecular footprints to predict at an unprecedented level of resolution if an individual is likely to have severe COVID or long COVID, and when they're likely to need a booster vaccine to maintain protection,” said professor Rao.

Professor Rao was awarded MTPConnect’s Redi Fellowship, which will allow her to work part-time at Max Kelsen’s Brisbane team offices.

Professor Rao and Max Kelsen experts will analyse multi-dimensional epigenetic and clinical data using ML models to analyse how thousands of signals from proteins, genes, individual patient clinical profiles, and how other factors interact to potentially cause one person to have a more severe reaction.

The goal is to identify a set of variables that together can identify individuals who are at risk of developing severe symptoms from SARS-CoV-2 infections.

Another goal is to accelerate the development of a clinic-ready AI-driven blood test for severe COVID-19 to predict individual risk of developing a severe disease in a community setting or when hospitalised.

This test may also be applied to understanding an individual’s resistance to infection by COVID-19.

Without machine learning, it would potentially take months to isolate possible causal factors at the epigenetic, biological and clinical level, using traditional regression analysis, Max Kelsen says.

Professor Rao and the team at Max Kelsen will instead apply ML models to identify possible causal factors, utilising cloud-based storage and compute services. This approach, considered one of the first of its kind in the world, Max Kelsen claims, is designed to not only reduce the time required to isolate potential causal factors, but provide valuable knowledge transfer to Professor Rao on the application of ML to epigenetic and clinical research and on the commercial pathways for software as a medical device.

“Max Kelsen has a long history of collaborating with QIMR Berghofer to apply artificial intelligence and machine learning to try to solve some of the most intractable problems in life sciences – including cancer and glaucoma. The collaboration with Professor Rao, however, is a world leading initiative – applying ML to unravel the mysteries of why COVID-19 can make one person very sick, while another person of a similar age may not get severely ill. This is a wonderful example of the pioneering work being done in Australia by brilliant people like Professor Rao and we are excited to contribute to it,” said Max Kelsen head of research Maciej Trzaskowski.

An initiative of the Federal Minister for Health Greg Hunt, the $32 million Redi Fellowship Program is supported by MTPConnect’s Researcher Exchange and Development within Industry initiative funded by the Medical Research Future Fund.

The Redi Fellowship program provides financial support to companies in the medical technology, biotechnology and pharmaceuticals (MTP) sector to bring researchers, clinicians and MTP professionals in-house for up to twelve months to work on priority medical research projects.

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