Transfer Rule Learning for Knowledge Based Biomarker Discovery and Predictive Bio

描述（由申请人提供）：对临床研究中产生的生物医学数据进行预测建模，用于疾病的早期发现、监测和预后，是发现生物标志物的关键一步。由于这些数据通常是有误差的测量结果，而且与测量的变量数量相比，任何研究的样本量都非常小，因此这些数据集产生的模型的有效性和验证对发现一种疾病的可靠的歧视性标记具有重大影响。充分利用这些稀缺数据的一个重要机会是将来自多个相关数据集的信息结合起来，以更有效地发现生物标志物。由于为每一种感兴趣的疾病创建大型数据集的成本可能仍然令人望而却步，因此更有效地利用相关生物标志物发现数据集的方法仍然很重要。解决方案：该项目开发并应用了迁移规则学习（TRL），这是一个新的框架，用于从相关但独立的数据集中发现综合生物标志物，例如从类似生物标志物分析研究中生成的数据集。TRL通过提供自动化的方式来表达、验证和使用从一个数据集生成的先验假设，同时通过相关数据集学习新知识，从而缓解了数据稀缺的问题。这是第一个将迁移学习用于生物标志物发现的研究。与其他迁移学习方法不同，TRL以可解释的模块化分类规则的形式获取知识，并使用它们在新数据集上播种规则模型的学习。分类规则简化了歧视性标记的提取，并已成功地用于非整合方式的生物标记发现和验证。具体目的：本项目主要测试

DESCRIPTION (provided by applicant): Predictive modeling of biomedical data arising from clinical studies for early detection, monitoring and prognosis of diseases is a crucial step in biomarker discovery. Since the data are typically measurements subject to error, and the sample size of any study is very small compared to the number of variables measured, the validity and verification of models arising from such datasets significantly impacts the discovery of reliable discriminatory markers for a disease. An important opportunity to make the most of these scarce data is to combine information from multiple related data sets for more effective biomarker discovery. Because the costs of creating large data sets for every disease of interest are likely to remain prohibitive, methods for more effectively making use of related biomarker discovery data sets continues to be important. Solution: This project develops and applies Transfer Rule Learning (TRL), a novel framework for integrative biomarker discovery from related but separate data sets, such as those generated from similar biomarker profiling studies. TRL alleviates the problem of data scarcity by providing automated ways to express, verify and use prior hypotheses generated from one data set while learning new knowledge via a related data set. This is the first study of transfer learning for biomarker discovery. Unlike other transfr learning approaches, TRL takes knowledge in the form of interpretable, modular classification rules, and uses them to seed learning of a rule model on a new data set. Classification rules simplify the extraction of discriminatory markers, and have been used successfully for biomarker discovery and verification in a non-integrative fashion. Specific Aims: This project tests the main hypothesis that TRL provides a mechanism for transfer learning of classification rules between related source and target data sets that improve performance on the target data, compared to learning without transfer. TRL will be evaluated using cross-validation performance of classification accuracy and transfer measures, on related groups of existing biomarker discovery datasets obtained from multiple experimental platforms for lung cancer detection and prognosis. A new set of independent validation data will be generated for early detection of lung cancer to test the models generated on pilot data. Insights into the impact of different modeling algorithms on transfer outcomes will be gleaned. Significance: The TRL framework and tool are important for combined analysis and interpretation of clinical data, as they support incremental building, verification and refinement of rule models for predictive biomedicine. The application of TRL to real-world biomarker discovery datasets can yield insights into novel interactions involving known markers, and the most reliable biomarkers for early detection of disease, particularly lung cancer. This project has the potential to help create new diagnostic screening tools for lung cancer detection. It allows foundational understanding of the use of transfer learning for integrative biomarker discovery that could lead to novel technologies for combining information from data and prior knowledge. PUBLIC HEALTH RELEVANCE: This project will develop highly-needed computational methods for integrative biomarker discovery from related but separate data sets produced by predictive molecular profiling studies of disease. It will generate new experimental data for early detection of lung cancer, and has the potential to help create new diagnostic screening tools for lung cancer, a leading cause of death from cancer in the United States.