Conditional Quantile Random Forest with Biomedical and Biological Applications

随着生物数据集变得越来越大和越来越复杂，现代生物学和生物医学正在经历一波机器学习应用。其中，随机森林特别吸引人，在生物学研究、基因组数据分析和生物医学科学中得到了极大的欢迎。它们在为复杂的数据和关联建模方面提供了极大的灵活性，同时仍然享有一定程度的可解释性和透明的决策机制。该项目旨在开发一种新的条件分位随机森林(CQRF)框架，该框架在很大程度上概括了现有的方法。该项目将研究其在促进生物学和生物医学科学方面的潜力，重点应用于分析电子病历和基因组数据。一旦开展，这些研究工作可能会在生物医学科学中带来新的知识发现和新的精确干预。该项目还为研究生提供了研究培训机会。处理复杂和异质的关联是在大数据应用中获得有意义推理的主要挑战之一。包括随机森林在内的机器学习方法已经成功地捕捉到了协变量空间中的异质性。在许多应用中，研究人员观察到，感兴趣的关联可能会因结果而异。这项研究建立在递归抽样分区和预测的条件分位数回归的基础上，这进一步扩大了针对结果之间的异质关联进行调整的灵活性。内置的条件回归模型也使推理成为可能，这是机器学习方法在医学应用中的另一个关键考虑因素，但尚未得到很好的探索和理解。因此，条件分位随机森林提供了更准确的预测和风险评估，增强了生物标志物检测的统计能力，为治疗选择提供了一种新的途径。同时，开发的框架建立在具有高维交互功能的分位数模型的基础上，这在分位数回归文献中是新的，可以显著增强分位数回归在大规模应用中的能力。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Modern biology and biomedical science are experiencing a wave of machine learning applications as biological data sets become increasingly larger and more complex. Among them, random forest is particularly appealing and has gained great popularity in biology studies, genomic data analysis, and biomedical science. They offer great flexibility in modeling the complex data and associations, while still enjoy certain levels of interpretability and transparent decision mechanism. The project aims to develop a new framework of conditional quantile random forest (CQRF), which largely generalize the existing approaches. The project will investigate its potential in advancing biology and biomedical science with focused applications analyzing electronic medical records and genomic data. Once carried out, the research work potentially lead to new knowledge discoveries and new precision interventions in biomedical science. The project also provides research training opportunities for graduate students.Handling complex and heterogenous associations is one of the major challenges to obtain meaningful inferences in big data applications. Machine learning methods including random forest have been successful in capturing the heterogeneity in the covariate space. In many applications, researchers have observed that the associations of interest could vary by the outcomes. The research builds on the conditional quantile regression in recursive sampling partitions and predictions, which further extends the flexibility to adjust for the heterogeneous association across the outcomes. The build-in conditional regression models also make the inference possible, which is another critical consideration for the medical applications of machine learning approaches but have not been well explored and understood. As a result, the conditional quantile random forest provides a more accurate predictions and risk assessment, enhances the statistical power for detecting biomarkers, and provides a new way for treatment selection. In the meantime, the developed framework is built on a quantile model with high-dimensional interactive function, which is new in the literature of quantile regression and could significantly enhance the capacity of quantile regression in large-scale applications.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.