Predictive Methods for Analyzing High-throughput Data and Spatial-Temporal Data

高通量测序生物技术的加速发展使得收集高维分子水平谱（如基因表达）成为可能，在本提案中称为特征。识别与表型相关的相关特征是非常有趣的。癌症状况、健康状况)。许多研究人员提倡应用统计学习方法对高通量数据进行预测分析。预测分析结果可用于许多方面。例如，它们可用于诊断人类疾病，预测对某种药物的反应（个性化医疗）；它们可以用来为植物/动物育种者选择一个最优的基因子集进行进一步的实验；从良好的预测模型中提取的特征子集可以促进揭示表型的生物学机制。不幸的是，即使是非常简单的模型，在预测分析中，高维也会导致巨大的过拟合。发现错误预测特征/模式的可能性非常高。因此，在寻找更多的预测特征时，在预测分析中与错误发现作斗争是具有挑战性的。我的研究成果将包括用于诚实地测量所选特征的预测性（如错误率，AUC）的新工具，以及用于识别真正可预测特征和构建更清晰的表型预测模型的新工具。我还将在与人类健康和食品安全相关的各种科学问题中使用特定的高通量数据集进行预测分析，这将为这些领域带来新的科学发现和新的解决方案。

The accelerated development of high-throughput sequencing biotechnologies has made it affordable to collect high-dimensional molecular-level profiles, such as gene expression, which are called features in this proposal. It is of great interest to identify relevant features associated with a phenotype (eg. cancer status, health disorder). Many researchers have advocated to apply statistical learning methods to perform predictive analysis for high-throughput data. Predictive analysis results can be used in many ways. For example, they can be used to diagnose human diseases, to predict response to a medicine (personalized medicine); they can be used to choose an optimal gene subset for further experiments by plant/animal breeders; the subset of features extracted from good predictive models can facilitate the uncovering of the biological mechanism for a phenotype. Unfortunately, the high-dimensionality causes enormous overfitting in predictive analysis even with very simple models. The chance of finding false predictive features/patterns is extremely high. Therefore, it is challenging to fight against false discovery in predictive analysis when searching for more predictive features. My research outcomes will include new tools for honestly measuring predictivity (such as error rate, AUC) of selected features, and new tools for identifying truly predictive features and for building sharper predictive models for phenotypes. I will also practice predictive analysis with specific high-throughput datasets in a variety of scientific problems related to human health and food security, which will lead to new scientific discoveries and new solutions for these areas. In science, a theory is tested by performing predictions for observations in the future. Significant discrepancies between observations and predictions suggest that the theory is incorrect or flawed. Similarly, looking at out-of-sample predictions is a straightforward method for comparing and checking goodness-of-fit (GOF) of statistical models. Today, increasingly complex models are being proposed for a variety of correlated data such as, temporal, spatial, and repeated measurements data. More widely applicable predictive methods for comparing and checking such complex models are demanded. I will work to improve predictive model comparison and checking methods for generalized linear mixed models (GLMM) for datasets with correlation structure, and to release R add-on packages to facilitate the comparison and checking of GLMM. My research outcomes will include new tools for evaluating complex Bayesian/non-Bayesian models with correlated random effects. These new model evaluation tools will be essential for researchers in epidemiology, ecology, and environmental sciences. Improved modelling of the datasets from these areas will lead to more solid data analysis conclusions, which have essential impact on policy making in economic-social problems.