Last Updated: 06/02/2025
AI assisted genomic profiling for the personalisation of treatment and control of infections
Objectives
The project aims to integrate AI-assisted genomic profiling with whole-genome sequencing (WGS) to enhance the personalization of treatment and control of infections, particularly malaria and tuberculosis. It will also integrate AI-based AMR mutation and transmission discovery tools into the informatic profiling software, making them dynamic and potentially improving clinical and infection control decision making.
London School of Hygiene and Tropical Medicine (LSHTM), United Kingdom
Cost-effective and rapid whole-genome sequencing (WGS) technologies are now being rolled-out in clinical settings to prevent disease, diagnose and personalize treatment of patients. WGS has become a routine, fast and affordable diagnostic tool used in infectious disease settings, revolutionizing clinical decision making, public health surveillance and infection control. This utility has been demonstrated during the COVID-19 pandemic, where rapid WGS of SARS-CoV-2 genomes has assisted the detection of clinically important variants (e.g., omicron), informed transmission dynamics, and aided vaccine development. More generally, analysis of WGS data can rapidly infer pathogen “virulent” strain-types, predict drug or antimicrobial resistance (AMR), and identify outbreaks. To assist this WGS-based analysis, molecular barcodes to profile pathogens for AMR, geographical source (e.g., for identification of importation events) and transmissibility can be derived and linked to fast informatic software tools. However, with increasing WGS use in clinical settings, there is a need for AI methods to mine the resulting big data to update barcodes and infer transmission dynamics in (near) real time. This WGS profiling work in malaria and tuberculosis disease has established informative barcoding mutations, and developed world-leading informatics platforms (e.g., TB-Profiler) that have been applied globally (>100k tuberculosis bacteria with WGS, profiled across >35 countries). AI methods (e.g., neural networks) have also been applied to detect known and identify novel genes linked to AMR, thereby improving knowledge of underlying resistance mechanisms to improve barcodes. Working within established collaborations involving The UK Health Security Agency (UKHSA) and Health ministries in Asia (Philippines, Thailand, Vietnam), which are routinely using WGS-based diagnostics, the resulting AI-informatics platforms will be implemented in UK and infectious disease endemic settings, with the potential of extending them to other infections, leading to associated health and economic benefits. Further, all WGS data generated, and AI and informatics software developed, will put in the public domain, leading to positive impacts in other biomedical research and healthcare areas.
Aug 2022 — Feb 2023
$112,100
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