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

该博士项目将为开发，验证和操作“生活”系统评价（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（系统生活信息机器）计划的一部分；CAMARADES（实验研究动物数据荟萃分析和回顾的协作方法www.dcn.ed.ac.uk/camarades）倡议。SR是至关重要的研究工具，对循证决策很重要，然而，在临床前研究中，SR和meta分析（MA）的使用相对较新。进行SRs的方法已经发展得很好，但它们是资源密集型的（Tricco等人，2008年），而且它们完成所需的时间削弱了它们的有用性，因为它们一旦发表就经常过时（Shojania等人，2007年）。数据库中出版物数量的指数级增长加剧了这个问题（Bastian et al., 2010）。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技术用于实验室研究领域，以提供经验证据，从而可以做出决策，从而减少研究投资的浪费（Macleod et al., 2014）。活的sr （LSR）是不断更新的sr，纳入相关的新证据，因为它是可用的（Elliott等，2017）。允许人和机器以相互支持的方式交互和操作，以节省时间和提高准确性。lsr将在快速发展的研究领域发挥巨大作用，因为它们将提高SRs的效率和可持续性。该项目将解决活体生物数据LSR实施的剩余挑战；结果数据提取。与系统评价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，机器辅助方法与基于人群的方法相结合是最可行的解决方案。该项目的目的有三个方面：(i)更新和完善定制的机器辅助数据提取工具包，专门用于在体内疼痛神经生物学SR设置中的机器辅助结果数据提取。（ii）探索和准备基于人群的LRS工具包部署方法（iii）在对照试验中测试工具包，使用SyRF平台在疼痛神经生物学三个关键需求领域的范例自动化SRs背景下进行：(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规范性值。对有害热、冷和机械刺激的诱发肢体退缩是一种普遍存在且长期存在的测定啮齿动物感觉反应的方法。（ii）新兴数据：测量啮齿类动物的复杂行为学相关疼痛行为，并确定这些行为如何受到自发疼痛的干扰，是疼痛研究的一个新兴和快速发展的领域。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。这代表了开发基于机器学习的数字文件自动分析的机会，这可能会被引入到lsr中。在开源的在线SR软件中开发和整合机器辅助数据提取和MAs工具，对于促进准确和及时的证据合成的产生，以改善决策，以及为lsr的未来功能和成功做出贡献，将是有益和必要的。这些强大的工具将有助于理解指数级增长的数据体，最终使以前认为无法实现的目标成为可能

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resource intensive (Tricco et al., 2008) and the time they take to complete weakens their usefulness as they are often out of date once they have been published (Shojania et al., 2007).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