Reproducibility Assessment for Multivariate Assays

多变量测定的重现性评估

• 批准号: 8647816
• 负责人: Chris Fraley
• 金额: $ 13.11万
• 依托单位: INSILICOS
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-04-01 至 2015-09-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8647816
• 关键词: Address; Affect; Algorithms; Area; Bioinformatics; Biological Assay; Biological Factors; Biological Markers; Biology; ChIP-seq; Clinical; Cloud Computing; Data; Data Analyses; Decision Trees; Development; Diagnostic; Dimensions; Effectiveness; Evaluation; Evolution; Genomics; Goals; Guidelines; In Vitro; Investigation; Lasso; Lead; Literature; Machine Learning; Measurement; Measures; Medical Research; Methodology; Methods; Modeling; Monitor; Outcome; Performance; Phase; Play; Protocols documentation; Public Health; Publishing; ROC Curve; Reproducibility; Research Project Grants; Scheme; Services; Signal Transduction; Simulate; Small Business Innovation Research Grant; Source; Specific qualifier value; Staging; Statistical Methods; Statistical Models; Support System; Techniques; Technology; Therapeutic; Trees; Validation; analytical tool; base; clinical practice; cost; data mining; design; disease diagnosis; drug development; follow-up; forest; high throughput technology; improved; indexing; novel diagnostics; public health relevance; research study

Project Summary. This Small Business Innovation Research project addresses the problem of assessing reproducibility in analyzing high-throughput data. In feature selection for data with large numbers of fea- tures, it is well known that some features will appear to affect an outcome by chance, and that subsequent predictions based on these features may not be as successful as initial results would seem to indicate. Similarly, there are often multiple stages, and many parameters, involved in the multivariate assays de- signed to analyze high-throughput profiles. For example, good results achieved with a particular combina- tion of settings for an instance of cross-validation may not generalize to other instances. The objective of this proposal is to extend new statistical methods for assessing reproducibility in replicate experiments to the context of machine learning, and demonstrate effectiveness in this application. The machine-learning methods to be investigated will include random forests, supervised principal components, lasso penal- ization and support vector machines. We will use simulated and real data from genomic applications to show the potential of this approach for providing reproducibility assessments that are not confounded with prespecified choices, for determining biologically relevant thresholds, for improving the accuracy of signal identification, and for identifying suboptimal results. Relevance. Although today's high-throughput technologies offer the possibility of revolutionizing clinical practice, the analytical tools available for extracting information from this amount of data are not yet sufficiently developed for targeted exploration of the underlying biology. This project directly addresses the need to make what the FDA terms IVDMIA (In-Vitro Diagnostic Multivariate Index Assays) transparent, interpretable, and reproducible, and is thus an opportunity to improve analysis products and services provided to companies that identify, characterize, and validate biomarkers for clinical diagnostics and drug development decision points. The long-term goal of the proposed project is to develop a platform for biomarker discovery and integrative genomic analysis, with reproducibility assessment incorporated into multivariate assays. This will enable evaluation and improvement of approaches to detecting the biological factors that affect a particular outcome, and lead to more efficient and more effective methods for disease diagnosis, treatment monitoring, and therapeutic drug development.

项目摘要。这个小企业创新研究项目解决了评估问题， 高通量数据分析的可重复性。在大量有限元数据的特征选择中 然而，众所周知，某些特征似乎会偶然影响结果，并且随后的特征可能会影响结果。 基于这些特征的预测可能不像最初的结果所显示的那样成功。 类似地，在多变量测定中通常涉及多个阶段和许多参数， 签署了高通量分析例如，用特定的组合实现的良好结果- 用于交叉验证实例的设置的选择不能推广到其它实例。的目标 这一建议是将用于评估重复实验中再现性的新统计方法扩展到 机器学习的背景，并在此应用程序中证明有效性。机器学习 研究的方法将包括随机森林，监督主成分，套索刑罚， 化和支持向量机。我们将使用来自基因组应用的模拟和真实的数据， 显示了该方法提供重现性评估的潜力， 预先指定的选择，用于确定生物学相关阈值，用于提高信号的准确性 识别和识别次优结果。 本案无关虽然今天的高通量技术提供了革命性的临床应用的可能性， 实践中，可用于从这些数据中提取信息的分析工具还没有 足以有针对性地探索潜在的生物学。该项目直接针对 需要使FDA术语IVDMIA（体外诊断多变量指数测定）透明化， 可解释和可再现，因此是改进分析产品和服务的机会 提供给识别、表征和验证临床诊断生物标志物的公司， 药物开发决策点。拟议项目的长期目标是开发一个平台， 生物标志物发现和综合基因组分析，并将再现性评估纳入 多变量分析这将有助于评估和改进检测生物污染物的方法。 影响特定结果的因素，并导致更有效和更有效的疾病治疗方法 诊断、治疗监测和治疗药物开发。