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Collaborative Research: Subject-level Prediction and Application

合作研究：学科级预测与应用

基本信息

批准号：
1915976
负责人：
Jonnagadda Rao
金额：
$ 12万
依托单位：
University of Miami School of Medicine
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-08-01 至 2023-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1915976&HistoricalAwards=false
关键词：
Collaborative Research Subject level Prediction

项目摘要

Many practical problems are related to prediction, where the main interest is at the subject (for example personalized or precision medicine) or (small) sub-population (for example small community) level. In recent years, new and challenging problems have emerged from diverse fields such as business, social sciences, and health sciences. Examples may involve prediction of a health outcome for a new patient or perhaps prediction of a new school's response to efforts to educate children about smoking prevention. The investigators have shown in previous work called classified mixed model prediction (CMMP) that in such cases, it is possible to make substantial gains in prediction accuracy by identifying a class that a new subject belongs to. However, the scenarios under which CMMP currently operates are somewhat constrained and many real-life situations fall outside its scope. Given the tremendous gains in accuracy that are possible, it would be very valuable to develop further methodology and computational advances to deepen knowledge in this area. This project aims to make methodological advances of the classified mixed model prediction method into other types of subject-level prediction problems as well as to develop new inferential methods along the CMMP idea, by making the latter truly useful in practical situations. The basic idea of CMMP is to create a "match" between a group or cluster in the population for which one wishes to make prediction and a (massive) training dataset, with known groups or clusters. Once such a match is built, the traditional mixed model prediction method can be utilized to make accurate predictions. The practical challenges that will be solved in this project include i) how to deal with training data with unknown grouping; ii) how to deal with sparse, high dimensional covariates; iii) how to make better use of covariate information to improve accuracy of CMMP; and iv) how to provide accurate measures of uncertainty for CMMP-type predictions. Two important areas of application will be investigated. One is in precision medicine and health disparities focusing on the prediction of epigenetic markers using high dimensional genotype profiles. The other comes from the area of family economics using a large survey of data from China where predictions at finer levels of resolution (e.g., households) are of primary interest. Both applications will leverage important collaborations with practitioners and thus increase the impact of the work.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.

许多实际问题都与预测有关，其中主要关注的是学科(例如个性化或精准医学)或(小)亚群体(例如小社区)一级。近年来，商业、社会科学、健康科学等多个领域出现了新的具有挑战性的问题。例如，可以预测新患者的健康结果，或者预测一所新学校对儿童预防吸烟教育的反应。研究人员在之前名为分类混合模型预测(CMMP)的工作中表明，在这种情况下，通过识别新对象所属的类别，可以大幅提高预测精度。然而，CMMP目前运行的场景在某种程度上受到限制，许多现实生活中的情况都超出了它的范围。考虑到可能在准确性方面取得的巨大进展，进一步发展方法学和计算进步以深化这一领域的知识将是非常有价值的。该项目旨在将分类混合模型预测方法应用于其他类型的主题级预测问题，并沿着CMMP思想开发新的推理方法，使后者在实际情况中真正有用。CMMP的基本思想是在希望对其进行预测的种群中的组或簇与具有已知组或簇的(大规模)训练数据集之间创建“匹配”。一旦建立了这样的匹配，就可以利用传统的混合模型预测方法进行准确的预测。该项目将解决的实际挑战包括：i)如何处理具有未知分组的训练数据；ii)如何处理稀疏的高维协变量；iii)如何更好地利用协变量信息来提高CMMP的精度；以及iv)如何为CMMP类型的预测提供准确的不确定性度量。我们将研究两个重要的应用领域。一个是在精确医学和健康差异方面，重点是使用高维基因图谱预测表观遗传标记。另一种来自家庭经济学领域，使用中国的大量数据调查，在这些数据中，对更精细分辨率(例如家庭)的预测是主要关注的。这两个应用程序都将利用与实践者的重要合作，从而增加工作的影响。这一奖项反映了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。