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Using Machine Learning to Anticipate Antimicrobial Resistance for Pathogen Surveillance and Therapeutic Stewardship

使用机器学习预测抗生素耐药性以进行病原体监测和治疗管理

基本信息

批准号：
2116945
负责人：
金额：
--
依托单位：
London School of Hygiene & Tropical Medicine
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2018
资助国家：
英国
起止时间：
2018 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=studentship-2116945
关键词：
Using Machine Learning Anticipate Antimicrobial

项目摘要

Antimicrobial resistance (AMR) is a serious global health challenge, for both human and animal health. Antimicrobials are drugs that are used to fight pathogenic microbes (viruses, bacteria and parasites). The widespread use and misuse of these drugs has led to the emergence of resistance. One of the ways by which microbes become resistant to drugs and survive drug exposure is by undergoing genetic changes (mutations). There is an urgent need to identify and predict such mutations that lead to resistance before they become fixed in a given pathogen population. Technological advancements have led to rapid, cost effective, and extensive data collection in all aspects of health science. However, to fully exploit these information rich sources, it becomes imperative that corresponding computation techniques are also developed to enhance our understanding of complex biological problems. Artificial Intelligence through Machine learning (ML) offers a powerful way to expedite our understanding of these problems. As the name suggests, ML is a way to make computers learn from and discover patterns in data. The project aims to develop a robust and automated computational framework to identify new mutations in pathogens. Understanding the molecular consequences of these mutations will provide insight into the growing failure of antimicrobial therapy. Developing sophisticated machine learning approaches will accelerate the identification of new ways to detect and respond to AMR. This will have a direct impact in pathogen surveillance, aid stewardship and guide development of new interventions. This multi-disciplinary project integrates and enhances skills in maths, statistics and computation (BBSRC core skills - new ways of working) to leverage information from existing vast and diverse data sources. This will allow expansion and advancement of the vital skills necessary to promote the UK's bioscience research capabilities and bioeconomy (BBSRC core skills - vulnerable capabilities). Combatting AMR spans multiple BBSRC strategic priorities; the project focuses on understanding the basic bioscience of molecular variation (Strategic research priority 3 - bioscience for health) as applicable to AMR, thereby supporting improvements in both human and animal health (Strategic research priority 1 - agriculture and food security).

抗菌素耐药性(AMR)是一个严重的全球健康挑战，对人和动物的健康都是如此。抗菌剂是用来对抗病原微生物(病毒、细菌和寄生虫)的药物。这些药物的广泛使用和误用导致了耐药性的出现。微生物对药物产生抗药性并在药物暴露中生存的方法之一是经历基因变化(突变)。在这些突变在特定病原体群体中固定之前，迫切需要识别和预测这些导致抗药性的突变。技术的进步导致了卫生科学各个方面的快速、成本效益和广泛的数据收集。然而，为了充分利用这些丰富的信息来源，相应的计算技术也变得势在必行，以提高我们对复杂生物学问题的理解。通过机器学习的人工智能(ML)提供了一种强大的方式来加速我们对这些问题的理解。顾名思义，ML是一种让计算机从数据中学习并发现模式的方法。该项目旨在开发一个强大的自动化计算框架，以识别病原体的新突变。了解这些突变的分子后果将提供对抗菌治疗日益失败的洞察。开发复杂的机器学习方法将加快识别检测和应对AMR的新方法。这将对病原体监测、帮助管理和指导新干预措施的开发产生直接影响。这一多学科项目整合并加强了数学、统计和计算方面的技能(BBSRC的核心技能--新的工作方法)，以利用来自现有大量和多样化数据来源的信息。这将允许扩大和提高促进英国生物科学研究能力和生物经济(BBSRC核心技能-脆弱能力)所需的关键技能。防治AMR涉及BBSRC的多个战略优先事项；该项目侧重于了解适用于AMR的分子变异的基本生物科学(战略研究优先事项3--生物科学促进健康)，从而支持改善人类和动物健康(战略研究优先事项1--农业和粮食安全)。