Transfer Rule Learning for Knowledge Based Biomarker Discovery and Predictive Bio

描述(由申请人提供)：对来自临床研究的生物医学数据进行预测建模，以早期发现、监测和预测疾病，是发现生物标记物的关键一步。由于这些数据通常是容易出错的测量数据，与测量的变量数量相比，任何研究的样本量都很小，因此从这些数据集产生的模型的有效性和验证对发现可靠的疾病识别标记具有重要影响。最大限度地利用这些稀缺数据的一个重要机会是结合来自多个相关数据集的信息，以更有效地发现生物标记物。由于为每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记物发现数据集的方法仍然很重要。解决方案：该项目开发和应用转移规则学习(TRL)，这是一个从相关但独立的数据集(例如，从类似的生物标记物分析研究产生的数据集)中发现综合生物标记物的新框架。TRL通过提供自动方式来表达、验证和使用从一个数据集产生的先前假设，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。这是迁移学习在生物标记物发现中的首次研究。与其他Transfr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种子规则模型的学习。分类规则简化了区分标记的提取，并已成功地用于非一体化方式的生物标记发现和验证。具体目标：本项目测试主要目标 假设TRL提供了一种机制，用于在相关的源和目标数据集之间迁移分类规则的学习，与没有迁移的学习相比，该机制提高了目标数据的性能。TRL将在从多个肺癌检测和预后实验平台获得的现有生物标记物发现数据集的相关组上，使用分类准确性和转移措施的交叉验证性能进行评估。将生成一组新的独立验证数据，用于肺癌的早期检测，以测试根据试点数据生成的模型。将深入了解不同建模算法对转移结果的影响。意义：TRL框架和工具对于临床数据的组合分析和解释非常重要，因为它们支持用于预测生物医学的规则模型的增量构建、验证和细化。技术的应用 TRL到真实世界的生物标记物发现数据集可以深入了解涉及已知标记物的新颖交互作用，以及最可靠的疾病早期检测生物标记物，特别是肺癌。该项目有可能帮助创造新的肺癌检测诊断筛查工具。它使人们对综合生物标记物发现的转移学习的使用有了基础性的了解，这可能导致将来自数据的信息和先前知识相结合的新技术。 公共卫生相关性：该项目将开发急需的计算方法，从疾病预测性分子图谱研究产生的相关但独立的数据集中发现综合生物标记物。它将为早期产生新的实验数据 该系统可用于肺癌的检测，并有可能帮助创建新的肺癌诊断筛查工具，肺癌是美国主要的癌症死亡原因。

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.