Bayesian Rule Learning Methods for Disease Prediction and Biomarker Discovery

用于疾病预测和生物标志物发现的贝叶斯规则学习方法

• 批准号: 8024941
• 负责人: Vanathi Gopalakrishnan
• 金额: $ 28.25万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-15 至 2014-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8024941
• 关键词: Address; Amyotrophic Lateral Sclerosis; Area; Bayesian Method; Biological Markers; Breast; Breast Cancer Early Detection; Characteristics; Classification; Clinical; Collaborations; Coupled; Data; Data Analyses; Data Set; Decision Trees; Development; Diagnostic tests; Disease; Disease model; Early Diagnosis; Evaluation; Foundations; Goals; Human; Hybrids; Informatics; Internet; Knowledge; Knowledge Discovery; Lead; Learning; Machine Learning; Malignant Neoplasms; Malignant neoplasm of lung; Methodology; Methods; Modeling; Molecular; Nerve Degeneration; Outcomes Research; Performance; Programmed Learning; Proteomics; Protocols documentation; Publishing; Rare Diseases; Research; Sample Size; Sampling; Scientist; Screening procedure; Serum; Solutions; Source; Space Models; Structure; Techniques; Technology; Testing; Training; Translating; Trees; Uncertainty; Validation; Work; base; computer based statistical methods; data mining; design; fundamental research; high throughput analysis; high throughput technology; improved; information organization; insight; malignant breast neoplasm; novel; novel diagnostics; predictive modeling; programs; tool

DESCRIPTION (provided by applicant): The problem: High-throughput biomedical data from biomarker profiling studies aimed at early detection of diseases like lung cancer are accumulating rapidly. Although many popular machine learning methods have been utilized for analysis of such high-dimensional datasets, no single method has consistently outperformed others. Moreover, scientists have the need to simultaneously address two related tasks: disease prediction and biomarker discovery, using the same sets of data and tools. One way, as undertaken in this project, to address this need is to find the most accurate classifier for the disease from a given set of profiles and present the discriminative markers used in that model to the scientist for further verification. The large space of possible models coupled with the small sample size of the data make it hard to accurately estimate predictive accuracy. The solution: This project will develop, evaluate and refine novel Bayesian Rule Learning (BRL) methods that are algorithmically efficient, result in parsimonious models and accurately estimate predictive uncertainty from sparse biomedical datasets. BRL methods utilize a Bayesian score to evaluate rule models, thereby quantifying the uncertainty in the validity of the rule itself. This novel technique that combines the mathematical rigor of Bayesian network learning with rule-based modeling opens up a hitherto underexplored area of fundamental research in informatics involving such hybrid methodologies. Rules enable modular representation of knowledge and collaboration with scientists, as it is easier to present the model and extract markers both visually and computationally. Rule-based inference is also simpler and more tractable. The Bayesian approach enables prior knowledge to be incorporated and evaluated in a continual fashion with a human in the loop. The latter is very important for refinement of both tools and models. The specific aims: This project will test the hypothesis that the BRL methods developed and extended herein produce more accurate and parsimonious models for disease state prediction than other state-of-the-art machine learning methods. This project evaluates BRL methods and models using existing proteomic datasets for three diverse diseases - rare, neurodegenerative Amyotrophic Lateral Sclerosis (ALS), and the two most common cancers in the world, lung and breast cancers. Experimental verification will be performed using a new set of retrospectively collected breast cancer sera samples to evaluate model generalizability. The significance: This project will produce: (1) a novel biomedical data mining tool for analyzing data from biomarker profiling studies of any disease, (2) methodological insights into the applicability of this tool and current machine learning methods for such tasks, and (3) new data for research on the early detection of breast cancer. It has potential to help develop new diagnostic tests for early detection of ALS, lung and breast cancers and lays a firm foundation for building modeling frameworks that can incorporate both prior knowledge and data to provide the technological capability for combining evidence from multiple, heterogeneous sources.

描述（由申请人提供）： 问题：来自生物标志物分析研究的高通量生物医学数据旨在早日检测到肺癌等疾病。尽管已经使用许多流行的机器学习方法来分析此类高维数据集，但没有一种方法始终超过其他方法。此外，科学家需要使用相同的数据和工具共同解决两个相关任务：疾病预测和生物标志物发现。正如该项目中所采取的一种方法，可以从给定的一组概况中找到最准确的疾病分类器，并将该模型中使用的歧视性标记呈现给科学家以进行进一步验证。可能的模型的大空间加上数据的较小样本量使得难以准确估计预测精度。 解决方案：该项目将开发，评估和完善新颖的贝叶斯规则学习（BRL）方法，这些方法在算法上有效，从而产生了简约的模型，并从稀疏的生物医学数据集中准确估计了预测性不确定性。 BRL方法利用贝叶斯分数评估规则模型，从而量化规则本身的有效性的不确定性。这种新颖的技术将贝叶斯网络学习的数学严谨性与基于规则的建模相结合，从而为涉及这种混合方法的信息学基础研究开辟了一个迄今为止尚未倍增的领域。规则可以与科学家进行知识和协作的模块化表示，因为在视觉和计算上更容易呈现模型并提取标记。基于规则的推论也更简单，更可行。贝叶斯的方法使先验知识能够与人类在循环中的人类中持续合并和评估。后者对于两种工具和模型的完善非常重要。 具体目的是：该项目将检验以下假设：与其他最先进的机器学习方法相比，本文开发和扩展的BRL方法为疾病状态预测产生更准确和更简约的模型。该项目使用现有的三种疾病的现有蛋白质组学数据集评估BRL方法和模型 - 罕见的，神经退行性的肌萎缩性侧面硬化症（ALS），以及世界上肺部和乳腺癌的两种最常见的癌症。实验验证将使用一组新的回顾性收集的乳腺癌血清样品来评估模型的推广性。 意义：该项目将产生：（1）一种新型的生物医学数据挖掘工具，用于分析任何疾病的生物标志物分析研究中的数据，（2）（2）对此工具的适用性和当前机器学习方法对此类任务的适用性的方法论见解，以及（3）用于乳腺癌早期发现研究的新数据。它有可能帮助开发新的诊断测试，以早日检测ALS，肺和乳腺癌，并为建立建筑建模框架奠定了坚实的基础，这些框架可以纳入先验知识和数据，以提供结合来自多个，异质源的证据的技术能力。