Mar 24 2024Reviewed by Lily Ramsey, LLM The tool, known as PleioGraph, can analyze complex medical data 100 times faster than existing state-of-the-art methods used to detect biological networks which explain disease risk and inform drug discovery.
The technology uses bioXcelerate’s own machine learning algorithms to sweep huge amounts of biological data to help drug developers identify the genes, proteins and cells that interact with each other to cause disease, a process known as genetic colocalization. It provides quicker and more accurate insights about how diseases develop, helping to accelerate the drug development process.
Exceeding the performance of traditional methods, PleioGraph can provide significant cost savings and reduce the amount of time patients have to wait for improved treatments and medicines. Working with the world’s leading pharmaceutical companies, PleioGraph has processed vast datasets between 75 and 100 time faster than industry leading methods, reducing time to insight down from months to hours.
Founded by academics from the UK’s leading universities, including Cambridge, Edinburgh, Imperial College and Oxford, bioXcelerate aims to bridge the gap between academia and the pharmaceutical industry by fostering a symbiotic relationship that improves health outcomes and benefits society. The scale of data available is outpacing our ability to analyze it. While patterns explaining how diseases occur are hidden within our ever-increasing data-banks, AI technologies which quickly and accurately reveal these patterns are in short supply. At bioXcelerate, we are providing a solution to overcome these barriers.