Development and validation of tools for systematic review and meta-analyses of complex, biological data sets

开发和验证复杂生物数据集的系统审查和荟萃分析工具

• 批准号: 1814195
• 金额: --
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-1814195
• 关键词: Development; validation; tools; systematic; review

This PhD project will make a specific and unique contribution to developing, validating and operationalising "living" systematic review (SR) and meta-analysis analysis of complex biological datasets as part of the SLIM (Systemic Living Information Machine) programme; a CAMARADES (Collaborative Approach to Meta-Analysis and Review of Animal Data from Experimental Studies www.dcn.ed.ac.uk/camarades ) initiative. SRs are vital research tools and are important for evidence-based decision making however, in preclinical research, the use of SR and meta-analysis (MA) are relatively novel. The methods for the conduct of SRs are well developed but they are resource intensive (Tricco et al., 2008) and the time they take to complete weakens their usefulness as they are often out of date once they have been published (Shojania et al., 2007). This problem is being exacerbated by the exponentially increasing number of publications in databases (Bastian et al., 2010). To address the complexities and challenges of research involving animals, CAMARADES have adapted the SR and MA techniques for use in the laboratory research arena to provide empirical evidence from which decisions can be made thereby reducing waste of research investment (Macleod et al., 2014). Living SRs (LSR) are SRs that are continually updated, incorporating relevant new evidence as it becomes available (Elliott et al., 2017). Allowing humans and machines to interact and operate in mutually supportive ways to save time and improve accuracy. LSRs will be of great utility in fields of research which are moving quickly as they will increase efficiency and sustainability of SRs. This project will address a remaining challenge to implementation of LSR for in vivo biological data; outcome data extraction. Compared to the clinical data used in systematic review, basic science data is presented using a vast range and often idiosyncratic formats, therefore, a machine assisted approach coupled to crowd-based methods are the most feasible solution. The aims of this project are three-fold: (i)To update and refine the bespoke machine-assisted data extraction toolkit specifically for machine assisted outcome data extraction in an in vivo pain neurobiology SR setting. (ii)To explore and prepare crowd-based approaches for toolkit deployment in LRS (iii) To test the toolkit in controlled trials, conducted using the SyRF platform in the context of exemplar automated SRs in three key areas of need in pain neurobiology: (i) Existing data: Determine normative values of lab rodent sensory thresholds to thermal, mechanical and cold stimuli. Evoked limb withdrawal to noxious heat, cold and mechanical stimuli is a ubiquitous and long standing method for determining sensory responses in rodents. (ii) Emerging data: measuring complex ethologically relevant pain behaviours in rodents and determining how these are perturbed by spontaneous pain is an emerging and rapidly growing area of pain research. As this field continues to expand it will benefit from an LSR being instituted at an early stage. (iii) Future opportunities: Many areas of neuroscience, including pain, now use video recording of a range of animal behaviours. This represents an opportunity to develop machine learning based automated analysis of digital files which could potentially be introduced into LSRs. The development and incorporation of machine-assisted data extraction and MAs tools in open-source, online SR software would be beneficial and necessary to facilitate the production of accurate and timely evidence synthesis to improve decision making as well as contributing to the future functionality and success of LSRs. These powerful tools will assist in making sense of an exponentially growing body of data which will ultimately, make what was previously thought of as unattainable, attainable

该博士项目将为开发，验证和操作“活”系统综述（SR）和复杂生物数据集的元分析分析做出具体而独特的贡献，作为SLIM（系统生命信息机）计划的一部分; CAMARADES（实验研究动物数据的元分析和综述的合作方法www.dcn.ed.ac.uk/camarades）倡议。SR是重要的研究工具，对于循证决策非常重要，然而，在临床前研究中，SR和荟萃分析（MA）的使用相对较新。进行员工代表的方法已经很成熟，但需要大量资源（Tricco等人，2008），并且它们完成所花费的时间削弱了它们的有用性，因为它们一旦被出版就经常过时（Shojania等人，2007年）。数据库中出版物数量呈指数级增长加剧了这一问题（Bastian等人，2010年）。为了解决涉及动物的研究的复杂性和挑战，CAMARADES已经将SR和MA技术应用于实验室研究竞技场，以提供可以做出决策的经验证据，从而减少研究投资的浪费（麦克劳德等人，2014年）。活的SR（LSR）是不断更新的SR，在其变得可用时并入相关的新证据（Elliott等人，2017年）。允许人类和机器以相互支持的方式进行交互和操作，以节省时间并提高准确性。在发展迅速的研究领域，地方工作人员代表将具有很大的效用，因为它们将提高工作人员代表的效率和可持续性。本项目将解决活体生物学数据LSR实施的剩余挑战;结果数据提取。与系统性综述中使用的临床数据相比，基础科学数据使用广泛且通常特殊的格式呈现，因此，机器辅助方法结合基于人群的方法是最可行的解决方案。该项目的目标有三个方面：（i）更新和完善定制的机器辅助数据提取工具包，专门用于体内疼痛神经生物学SR设置中的机器辅助结局数据提取。（ii）探索和准备在LRS中部署工具包的基于人群的方法（iii）在对照试验中测试工具包，在疼痛神经生物学三个关键需求领域的示例自动SR背景下使用SyRF平台进行：（i）现有数据：确定实验室啮齿动物对热，机械和冷刺激的感觉阈值的标准值。热、冷、机械刺激引起的肢体退缩是一种普遍存在的、长期存在的啮齿类动物感觉反应测定方法。(ii)新出现的数据：测量啮齿类动物复杂的行为学相关疼痛行为并确定自发性疼痛如何干扰这些行为是疼痛研究的新兴和快速增长的领域。随着这一领域的不断扩大，它将受益于在早期阶段建立的LSR。(iii)未来的机会：神经科学的许多领域，包括疼痛，现在使用一系列动物行为的视频记录。这代表了一个开发基于机器学习的数字文件自动分析的机会，这些文件可能会引入到LSR中。在开放源代码的在线统计报告软件中开发和纳入机器辅助数据提取和管理和评估工具，将是有益的，也是必要的，有助于制作准确和及时的证据综合，以改进决策，并有助于地方统计报告的未来功能和成功。这些强大的工具将有助于理解呈指数级增长的数据体，这些数据体最终将使以前认为无法实现的东西变得可以实现