Enabling the big data revolution through skills training

通过技能培训推动大数据革命

• 批准号: MR/V038664/1
• 负责人: Rebecca Oakey
• 金额: $ 92.18万
• 依托单位: King's College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FV038664%2F1
• 关键词: Enabling; big; data; revolution; through

Tremendous amounts of data are produced every day in the life sciences and biomedicine and made available to the wider biological research community in the form of databases, data records and digitised information. The heterogeneity of these data, and at the same time, the potential amount of information that they can give us is astoundingly complex. We are at a crucial moment in biomedical research for which the effort of multiple disciplines in the quantitative and in the biomedical sciences have to come together to rationalise, quantify and extract the essential information content that can be translated into practical use in the medical context.We present a flexible training programme to facilitate the understanding of the data landscape that is populating modern medicine. More accurate and informed diagnoses will be possible if all the involved parties are able to extract useful information from patient data records and if these are efficiently integrated with genetic and analytical investigations with the aim of designing personalised therapies. The program is aimed at a large set of trainees: from medical practitioners, clinicians, scientists, companies and workers in the health sector. The courses will focus on skills training of different complexity that can be assembled in a personalised modular fashion. The flexibility is in the opportunity to pick and mix courses to generate learning curricula of different depth levels that can be started at any point. The offered courses will range from data exploration, integration and manipulation to more in depth analyses via computational statistical and artificial intelligence (AI) based methods. The trainees will have the opportunity to participate in the assembly of computational pipelines to analyse the data, and to bring to the table their own data for collaborative analyses.We have designed the training programme centred around three pillars (workstreams) that we believe are among our strengths in terms of training expertise, data collection and method development: Health Data Science exploring electronic data records (WS1); 'Omics harnessing genetics and molecular data collected in online databases (WS2); Artificial Intelligence focusing data image analysis and understanding AI through practical applications (WS3). The cross-talk between these areas of research is only at the beginning, this programme should facilitate collaborative efforts in identifying and overcoming the barriers for effective integration and translation across disciplines.The programme will engage the supporters, the patients and the public in workshops led by the participants sharing their learning experience and feedback suggestions for the structure and contents of the thought material.The program has three levels of governance: A) a management committee of PI, co-I's and WS leads; B) a stakeholder committee of representatives from academic research including ECRs, mid-career and senior leaders, clinical trainees and clinical academics and industry-based trainees; C) an advisory group with representatives invited from the funder and project partners to feedback information in a loop from which the program will continuously learn and improve.

生命科学和生物医学领域每天产生大量数据，并以数据库、数据记录和数字化信息的形式提供给更广泛的生物研究界。这些数据的异质性，与此同时，它们能提供给我们的潜在信息量是惊人的复杂。我们正处于生物医学研究的关键时刻，定量和生物医学科学的多个学科必须共同努力，使基本信息内容合理化，量化和提取，这些内容可以转化为医学背景下的实际应用。我们提出了一个灵活的培训计划，以促进对数据景观的理解，这是填充现代医学。如果所有相关方都能从患者数据记录中提取有用的信息，如果这些信息能有效地与基因和分析调查结合起来，以设计个性化的治疗方法，那么更准确和知情的诊断将是可能的。该方案的对象是一大批受训者：来自医疗从业人员、临床医生、科学家、公司和卫生部门的工作人员。这些课程将侧重于不同复杂程度的技能培训，这些技能可以以个性化的模块化方式组合起来。灵活性在于有机会选择和混合课程，以生成可以在任何时候开始的不同深度的学习课程。提供的课程将从数据探索、整合和操作到通过计算统计和基于人工智能（AI）的方法进行更深入的分析。学员将有机会参与计算管道的组装以分析数据，并将他们自己的数据带到桌面上进行协作分析。我们围绕三个支柱（工作流程）设计了培训方案，我们认为这是我们在培训专业知识、数据收集和方法开发方面的优势：探索电子数据记录的健康数据科学（WS1）；利用在线数据库收集的遗传学和分子数据的组学（WS2）；人工智能聚焦数据图像分析，通过实际应用理解人工智能（WS3）。这些研究领域之间的交流才刚刚开始，该方案应促进合作努力，以确定和克服跨学科有效整合和翻译的障碍。该计划将邀请支持者、患者和公众参加由参与者主持的研讨会，分享他们的学习经验，并对思想材料的结构和内容提出反馈意见。该项目有三个层次的管理：A) PI、co-I和WS领导组成的管理委员会；B)由学术研究代表组成的利益相关者委员会，包括ecr、职业生涯中期和高级领导、临床实习生、临床学者和行业实习生；C)由资助者和项目合作伙伴邀请的代表组成的咨询小组，在一个循环中反馈信息，从而使项目不断学习和改进。