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）： 问题是：旨在早期检测肺癌等疾病的生物标志物分析研究的高通量生物医学数据正在迅速积累。尽管许多流行的机器学习方法已被用于分析此类高维数据集，但没有一种方法能够始终优于其他方法。此外，科学家需要使用相同的数据集和工具同时解决两项相关的任务：疾病预测和生物标志物发现。正如本项目所采取的，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，并向科学家提供该模型中使用的判别标记以供进一步验证。可能的模型空间很大，加上数据样本量很小，因此很难准确估计预测的准确性。 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，这些方法在算法上高效，产生简约的模型，并根据稀疏的生物医学数据集准确估计预测不确定性。 BRL 方法利用贝叶斯评分来评估规则模型，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，开辟了涉及此类混合方法的信息学基础研究迄今为止尚未开发的领域。规则可以实现知识的模块化表示以及与科学家的协作，因为更容易以视觉和计算方式呈现模型并提取标记。基于规则的推理也更简单、更容易处理。贝叶斯方法使得先验知识能够以持续的方式与人在循环中结合和评估。后者对于工具和模型的细化非常重要。 具体目标：该项目将测试以下假设：与其他最先进的机器学习方法相比，本文开发和扩展的 BRL 方法能够产生更准确、更简洁的疾病状态预测模型。该项目使用现有的蛋白质组数据集评估三种不同疾病的 BRL 方法和模型：罕见的神经退行性肌萎缩侧索硬化症 (ALS) 以及世界上两种最常见的癌症：肺癌和乳腺癌。将使用一组新的回顾性收集的乳腺癌血清样本进行实验验证，以评估模型的普遍性。 意义：该项目将产生：（1）一种新颖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的数据，（2）对该工具的适用性以及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）乳腺癌早期检测研究的新数据。它有潜力帮助开发新的诊断测试，以早期检测 ALS、肺癌和乳腺癌，并为构建建模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异质来源的证据的技术能力。