Principled Methods for Very Large-Scale Causal Discovery

超大规模因果发现的原则方法

• 批准号: 6930544
• 负责人: Constantin F. Aliferis
• 金额: $ 19.93万
• 依托单位: VANDERBILT UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-08-01 至 2008-07-14
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6930544
• 关键词: artificial intelligence; biomedical automation; breast neoplasms; cell line; computer data analysis; computer program /software; disease /disorder etiology; disease /disorder model; gene expression; health science research; human data; information systems; lung neoplasms; mathematical model; medical records; method development; microarray technology; neoplasm /cancer genetics; polymerase chain reaction; western blottings

DESCRIPTION (provided by applicant): The long-term goal of the research proposed here is to develop, validate and apply methods for very large-scale principled causal discovery that scale up to massive datasets such as the ones found in bioinformatics, electronic patient records, and bibliographic systems. The explosive proliferation and growth (in sample, variables, and quality) of such datasets creates tremendous opportunities for biomedical discoveries, hence powerful methods for causal discovery have the potential to revolutionize biomedicine. To address this problem of scale, the co-PIs have developed several novel causal discovery algorithms with well-defined properties and guarantees that employ a principled local approach: these algorithms focus only on the local causal neighborhood (e.g. direct causes and effects or, alternatively, Markov Blanket) of a single or several "target" variable(s), and they are built on a formal framework for representing and learning causality. A plethora of preliminary experiments with simulated and real data suggest that the algorithms are sound and highly scalable. The local algorithms, by their assumptions, are expected to have applicability to a broad application context that includes bioinformatics, epidemiology, text analysis, and clinical medicine. The proposed research intends to take two focused steps in this broad application space. The local algorithms will be applied to (a) gene expression data from patients with lung cancer and (b) data from a large epidemiologic analysis of factors that influence development of breast cancer in patients with non-invasive breast disease. It is hypothesized that novel and potentially significant new causal relationships will be discovered. This hypothesis bears great biomedical and methodological significance. The specific aims are to (i) validate the novel causal algorithms; (ii) induce novel hypotheses about the immediate causes and effects of a selected group of genes implicated in lung cancer; (iii) induce novel causal hypotheses about the causes of breast cancer; (iv) compare the performance of the novel local algorithms to state-of-the-art alternatives; (v) disseminate new and powerful causal discovery tools. The methods to evaluate the novel causal algorithms and the hypotheses generated by them are: (a) validation against existing knowledge using structured, evidence-based, blinded literature review by domain experts; (b) selective experimentation in cell lines (lung cancer domain), and (c) statistical performance metrics.

描述（由申请人提供）： 这里提出的研究的长期目标是开发、验证和应用用于大规模原则性因果发现的方法，这些方法可以扩展到海量数据集，例如生物信息学、电子病历和书目系统中发现的数据集。此类数据集的爆炸性扩散和增长（在样本、变量和质量方面）为生物医学发现创造了巨大的机会，因此强大的因果发现方法有可能彻底改变生物医学。 为了解决这个规模问题，共同PI开发了几种新颖的因果发现算法，这些算法具有明确定义的属性，并保证采用有原则的局部方法：这些算法仅关注局部因果邻域（例如直接原因和影响，或者， 、马尔可夫毯子）的单个或多个“目标”变量，并且它们建立在用于表示和学习因果关系的正式框架上。使用模拟和真实数据进行的大量初步实验表明，这些算法是合理的且具有高度可扩展性。 根据他们的假设，本地算法预计适用于广泛的应用环境，包括生物信息学、流行病学、文本分析和临床医学。拟议的研究打算在这个广泛的应用领域采取两个重点步骤。本地算法将应用于（a）来自肺癌患者的基因表达数据和（b）来自对影响非侵入性乳腺疾病患者乳腺癌发展的因素的大型流行病学分析的数据。假设将会发现新颖且潜在重要的新因果关系。这一假说具有重大的生物医学和方法论意义。具体目标是（i）验证新颖的因果算法； (ii) 提出关于与肺癌有关的一组选定基因的直接原因和影响的新假设； (iii) 提出关于乳腺癌原因的新的因果假设； (iv) 将新颖的本地算法的性能与最先进的替代算法进行比较； (v) 传播新的、强大的因果发现工具。评估新颖因果算法及其产生的假设的方法是：（a）由领域专家使用结构化、循证、盲法文献综述对现有知识进行验证； （b）细胞系（肺癌领域）的选择性实验，以及（c）统计性能指标。