OutSee Limited

Commercial

OFHS240125

OutSee has developed a new AI approach called Nomaly which uses cutting edge scientific information to predict the impact of genetic change in the health of an individual. When we make a prediction that is consistent with an individual's health records, Nomaly can explain the biology that is causing their disease. This makes it easier to develop drugs against that disease. We will run Nomaly on the Our Future Health dataset to develop this method. We will use genotypes to make predictions, and use comparisons to the health records to improve the prediction process. And when predictions are good, we will use the models to gain insights into the related disease. Our Future Health is ideal for our research because it has linked genetic and health information. OFH has many participants which means we can study rarer diseases, and identify smaller subgroups of common diseases. Besides further developing and validating our method, the results of our work will be a set of predictions of the genetic and biological mechanism of diseases. In the long term, if the predictions are backed up by lab work, we will work with large pharmaceutical companies to develop new drugs using our insights. While we know that genetic variations influence disease, there remains a gap between the genetic changes know to influence a disease, and the amount of genetic changes that were supposed to influence a disease (the heritable part of a disease). Our research aims to address this knowledge gap. We designed the approach (Nomaly) to bridge this gap by predicting consequences of genetic changes from fundamental molecular biological knowledge, and evaluates predictions based on biological relevance to a disease rather than just statistical association. We want to apply Nomaly to the OFH data because it is a unique approach, so we think it will be valuable for patients. By providing additional and orthogonal insights to statistical association tests, the study outputs will be valuable for drug developers and patients. The anticipated research significance includes providing mechanistic understanding of how gene interactions contribute to disease. We also anticipate potential applications in disease research and precision medicine, aiding in the discovery of genetic variants with significant biological effects, which can lead to potential therapeutic targets and patient stratification markers.

We still don’t fully understand how specific genetic changes lead to disease. This gap limits our ability to develop better treatments, and to target treatments to the right patients (personalised, or precision medicine). Existing genetic analysis methods focus on finding associations between genes and diseases, meaning which gene changes are most often found in people with diseases. This don’t always explain the underlying biological mechanisms causing the disease. Our approach, Nomaly, goes beyond associations to predict how genetic changes may cause disease. We have more confidence in these predictions if they match the patient’s actual diseases as found in the health records. By identifying which genes cause diseases and how, our research could improve disease diagnosis, lead to more targeted treatments, and help in the development of new and personalised medicines. Because our technology is not limited to a specific disease, it can be applied to a wide range of conditions which don’t current have effective medicines—especially those where we suspect that genetics plays a key role, but the existing methods have failed to provide answers.

Public Health Research

02/06/2025

21/05/2025

TRE