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

作为SLIM(系统生命信息机)计划的一部分，这一博士项目将为开发、验证和实施复杂生物数据集的“生命”系统审查(SR)和荟萃分析做出具体而独特的贡献；Camarade(实验研究动物数据荟萃分析和审查的合作方法)计划www.dcn.ed.ac.uk/camarade。SR是重要的研究工具，对循证决策很重要，然而，在临床前研究中，SR和荟萃分析(MA)的使用相对较新。开展工作人员代表报告的方法发展得很好，但它们是资源密集型的(Tricco等人，2008年)，而且完成这些方法所需的时间削弱了它们的有用性，因为它们一旦公布往往就过时了(Shojania等人，2007年)。数据库中的出版物数量呈指数级增长，加剧了这一问题(Bastian等人，2010年)。为了应对涉及动物的研究的复杂性和挑战，Camarade已将SR和MA技术用于实验室研究领域，以提供可供决策的经验证据，从而减少研究投资的浪费(Macleod等人，2014年)。活着的SRS(LSR)是不断更新的SRS，在获得相关新证据时纳入其中(Elliott等人，2017年)。允许人和机器以相互支持的方式交互和操作，以节省时间和提高精度。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。该项目将解决实施体内生物数据的LSR的一个剩余挑战：结果数据提取。与系统综述中使用的临床数据相比，基础科学数据的呈现范围很广，而且往往是独特的格式，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。这个项目的目标有三个：(I)更新和完善定制的机器辅助数据提取工具包，专门用于在活体疼痛神经生物学SR环境中进行机器辅助结果数据提取。(Ii)探索和准备在LRS中部署工具包的基于人群的方法(Iii)在疼痛神经生物学的三个关键需要领域中使用SYRF平台在示范自动化SRS的背景下进行的对照试验中测试工具包：(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。伤害性热、冷和机械刺激诱发的肢体退缩是测定啮齿动物感觉反应的一种普遍且由来已久的方法。(2)新兴数据：测量啮齿动物复杂的与行为相关的疼痛行为，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增长的领域。随着这一领域的继续扩大，它将受益于在早期阶段建立的LSR。(3)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。这是一个开发基于机器学习的数字文件自动分析的机会，可能会被引入LSR。在开源的在线SR软件中开发和纳入机器辅助数据提取和MAS工具将是有益的，也是必要的，有助于产生准确和及时的证据合成，以改进决策，并有助于LSR未来的功能和成功。这些强大的工具将帮助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实现

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resource intensive (Tricco et al., 2008) and the time they take to complete weakens their usefulness as they are often out of date once they have been published (Shojania et al., 2007).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