Enabling the big data revolution through skills training

生命科学和生物医学每天产生大量数据，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，以及它们可以为我们提供的潜在信息量，令人震惊地复杂。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中的多个学科的努力必须共同努力，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领域的实际应用。我们提出了一个灵活的培训计划，以促进对现代医学中数据格局的理解。如果所有相关各方都能够从患者数据记录中提取有用的信息，并将这些信息与基因和分析调查有效地结合起来，以设计个性化的治疗方法，那么就有可能做出更准确和更知情的诊断。该计划针对的是一大批实习生：来自医生、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，这些培训可以以个性化的模块化方式进行组装。灵活性在于有机会挑选和混合课程，以产生不同深度水平的学习课程，这些课程可以在任何时候开始。提供的课程范围从数据探索、集成和处理，到通过基于计算统计和人工智能(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提交到桌面上进行协作分析。我们围绕三个支柱(工作流)设计了培训计划，我们认为这三个支柱在培训专业知识、数据收集和方法开发方面是我们的优势之一：健康数据科学探索电子数据记录(WS1)；利用在线数据库中收集的遗传学和分子数据的OMICS(WS2)；专注于数据图像分析的人工智能，以及通过实际应用理解人工智能(WS3)。这些研究领域之间的相互交流仅仅是开始的，这个计划应该促进合作努力，以确定和克服有效整合和跨学科翻译的障碍。该计划将使支持者、患者和公众参加由参与者领导的研讨会，分享他们的学习经验，并就思想材料的结构和内容提出反馈建议。该计划有三个层次的管理：A)管理委员会，由PI、Co-I和WS Leads组成；B)学术研究代表的利益相关者委员会，包括ECR、职业中期和高级领导人、临床实习生和临床学者以及以行业为基础的实习生；C)一个由资助者和项目合作伙伴邀请的代表组成的咨询小组，以循环的方式反馈信息，方案将不断从中学习和改进。

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.