Bayesian Rule Learning Methods for Disease Prediction and Biomarker Discovery

描述(由申请人提供)： 问题是：旨在早期发现肺癌等疾病的生物标记物分析研究的高通量生物医学数据正在迅速积累。虽然许多流行的机器学习方法已被用于分析这样的高维数据集，但没有一种方法始终优于其他方法。此外，科学家需要同时处理两项相关的任务：疾病预测和生物标记物发现，使用相同的数据和工具。如本项目所述，解决这一需求的一种方法是从一组给定的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提交给科学家进行进一步验证。可能的模型空间很大，再加上数据的样本量很小，因此很难准确地估计预测精度。 解决方案：该项目将开发、评估和改进新的贝叶斯规则学习(BRL)方法，这些方法在算法上高效，产生简洁的模型，并准确地估计稀疏生物医学数据集的预测不确定性。BRL方法利用贝叶斯分数来评估规则模型，从而量化规则本身有效性的不确定性。这种新技术将贝叶斯网络学习的数学严谨性与基于规则的建模结合在一起，开辟了迄今为止未被探索的信息学基础研究领域，涉及这种混合方法。规则支持知识的模块化表示和与科学家的合作，因为它更容易呈现模型并从视觉和计算上提取标记。基于规则的推理也更简单、更容易处理。贝叶斯方法使先验知识能够在有人参与的情况下以连续的方式被结合和评估。后者对于工具和模型的改进都非常重要。 具体目标：这个项目将测试这样一个假设，即本文开发和扩展的BRL方法比其他最先进的机器学习方法产生更准确和简洁的疾病状态预测模型。该项目使用三种不同疾病的现有蛋白质组数据集来评估BRL方法和模型-罕见的神经退行性肌萎缩侧索硬化症(ALS)，以及世界上最常见的两种癌症-肺癌和乳腺癌。将使用一组新的回溯收集的乳腺癌血清样本进行实验验证，以评估模型的泛化能力。 意义：该项目将产生：(1)一种新的生物医学数据挖掘工具，用于分析任何疾病的生物标记物分析研究的数据；(2)对该工具和当前机器学习方法在此类任务中的适用性的方法论见解；以及(3)用于乳腺癌早期检测研究的新数据。它有可能帮助开发新的诊断测试，用于ALS、肺癌和乳腺癌的早期检测，并为建立模型框架奠定坚实的基础，该框架可以整合先验知识和数据，以提供组合来自多个不同来源的证据的技术能力。

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.