Probabilistic knowledge representation of big data for efficient integration and useful inference in bio-medicine

大数据的概率知识表示，用于生物医学中的有效集成和有用推理

• 批准号: 1949035
• 金额: --
• 依托单位: University of Manchester
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-1949035
• 关键词: Probabilistic; knowledge; representation; big; data

Background: In order to realise the opportunities offered by big data in the field of biomedicine,these must be combined, integrated, and made available for cross-cutting research. To achieve this,many issues stemming from the inherent heterogeneity of datasets, such as the use of different datamodels, naming conventions and levels of abstraction, must be overcome. Traditional approachesaimed at improving compatibility of discrete datasets have focused on establishing universalstandards for data representation at the source; for example, by using standard vocabularies orontologies to consistently and unambiguously represent concepts of interest. However, while suchapproaches are ideal in theory, they have had few successful examples. Universal standards arepoorly adopted in the biomedical community. Alternatively, conventional integration methods arelaborious and usually lead to reduction in dimensionality and loss of information in the integrateddataset. We propose the development of a Bayesian approach to harmonisation and dataintegration, allowing for more inclusive and flexible knowledge representation.Hypothesis: Novel methods for data integration, adopting a probabilistic approach, can improve theinference from heterogeneous datasets for biomedical research.Objectives:1. Review existing literature of probabilistic learning/mining methodologies to identify potentialstarting points for this project.2. Extend and/or develop methods for generic data description and visualisation, to assist in rapidexploration of new datasets.3. Develop a Bayesian approach for knowledge representation and integration.4. Evaluate performance, accuracy and limitations, and compare to existing integration methods.5. Apply newly developed methods to real-world datasets in biomedicine and health such ascancer clinical trials and studies in Lupus.Methods: This project requires the development, application, and evaluation of computationalmethods in machine learning, data processing, and knowledge representation. The student will alsobe exposed to methods for data analysis and data mining in the biomedical domain. The Farr@HeRCdeveloped eLab platform, adopted by multiple projects to integrate data gathered acrossinternational studies, will be used as a testbed for the developed methods.Outcomes/Impact:1. A new framework for efficient, probabilistic data integration, with an evaluation of itsperformance, accuracy and limitations.2. New approaches for knowledge representation in the biomedical domain, with potential for wider adoption in other fields.3. One or more applied use cases that demonstrate the utility of the developed methodologies.Training:Dr Niels Peek - Informatics and machine learning input.Dr Nophar Geifman - Informatics, knowledge representation and biomedical input.Dr Philip Couch - Information sciences input.The student will sit in the MRC-funded Farr@HeRC, with exposure to a range of informatics,statistics, clinical, and epidemiological expertise. He/she will belong to the HeRC Doctoral TrainingNetwork and will have the opportunity to receive training as part of this, as well as other in housetraining such as CPD programmes and MSc Health Data Science modules as required

背景资料：为了实现大数据在生物医学领域提供的机会，必须将这些数据结合、整合并提供给交叉研究。为了实现这一目标，必须克服数据集固有的异质性所产生的许多问题，例如使用不同的数据模型、命名约定和抽象层次。传统的方法旨在提高离散数据集的兼容性，重点是在源处建立数据表示的通用标准;例如，通过使用标准词汇表或本体来一致且明确地表示感兴趣的概念。然而，尽管这些方法在理论上是理想的，但它们很少有成功的例子。生物医学界普遍采用的标准很少。或者，传统的集成方法是费力的，通常会导致减少维数和损失的信息在integrateddataset。我们提出了一个贝叶斯方法的协调和数据集成的发展，允许更包容和灵活的知识representation.Hypothesis：数据集成的新方法，采用概率的方法，可以提高theference从异构数据集的生物医学研究。回顾概率学习/挖掘方法的现有文献，以确定本项目的潜在起点.扩展和/或开发通用数据描述和可视化的方法，以帮助快速开发新产品。3.开发贝叶斯方法用于知识表示和集成.评估性能、准确性和局限性，并与现有的集成方法进行比较。5.将新开发的方法应用于生物医学和健康领域的真实数据集，例如癌症临床试验和Lupus.Methods研究：该项目需要开发，应用和评估机器学习，数据处理和知识表示中的计算方法。学生还将接触到生物医学领域的数据分析和数据挖掘方法。Farr@ HeRC开发的eLab平台被多个项目采用，用于整合跨国际研究收集的数据，将被用作开发方法的测试平台。一个新的框架，有效的，概率数据集成，其性能，准确性和局限性的评估.生物医学领域知识表示的新方法，有可能在其他领域得到更广泛的采用。培训：Niels Peek博士-信息学和机器学习输入; Nophar Geifman博士-信息学、知识表示和生物医学输入; Philip Couch博士-信息科学输入。学生将坐在MRC资助的Farr@HeRC，接触一系列信息学、统计学、临床和流行病学专业知识。他/她将属于HeRC博士培训网络，并将有机会接受培训，作为其中的一部分，以及其他内部培训，如CPD计划和MSc健康数据科学模块