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Adaptive Reproducible High-Dimensional Nonlinear Inference for Big Biological Data

生物大数据的自适应可再现高维非线性推理

基本信息

批准号：
9674585
负责人：
Yingying Fan
金额：
$ 28.97万
依托单位：
UNIVERSITY OF SOUTHERN CALIFORNIA
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-08-01 至 2022-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9674585
关键词：
Address Algorithms Archaea Attention Bacteria Big Data Biological Bypass Cells Colorectal Cancer Complex Computer software Consult Coupled Data Data Set Development Dimensions Disease Ecosystem Effectiveness Environment Foundations Frequencies Gaussian model Genes Genetic Materials Genomics Healthcare Human Internet Investigation Joints Length Linear Regressions Literature Liver Cirrhosis Machine Learning Marines Mathematics Metagenomics Methods Modeling Modernization Molecular Molecular Sequence Data Mutation Neurosciences Non-Insulin-Dependent Diabetes Mellitus Non-linear Models Obesity Organism Performance Planet Earth Play Procedures Reproducibility Reproducibility of Results Research Research Personnel Role Sampling Sampling Studies Shotguns Social Sciences Testing Theoretical Studies Tissues Training Viral Virus Visualization software Work base biological research computerized tools dark matter deep learning design flexibility high dimensionality human disease human tissue improved interest learning strategy metagenomic sequencing microbial community microbiome microbiome research model design model development new technology novel power analysis response simulation theories trait user-friendly virus host interaction virus identification

项目摘要

Big data is now ubiquitous in every field of modern scientific research. Many contemporary applications, such as the recent national microbiome initiative (NMI), greatly demand highly flexible statistical machine learning methods that can produce both interpretable and reproducible results. Thus, it is of paramount importance to identify crucial causal factors that are responsible for the response from a large number of available covariates, which can be statistically formulated as the false discovery rate (FDR) control in general high-dimensional nonlinear models. Despite the enormous applications of shotgun metagenomic studies, most existing investigations concentrate on the study of bacterial organisms. However, viruses and virus-host interactions play important roles in controlling the functions of the microbial communities. In addition, viruses have been shown to be associated with complex diseases. Yet, investigations into the roles of viruses in human diseases are significantly underdeveloped. The objective of this proposal is to develop mathematically rigorous and computationally efficient approaches to deal with highly complex big data and the applications of these approaches to solve fundamental and important biological and biomedical problems. There are four interrelated aims. In Aim 1, we will theoretically investigate the power of the recently proposed model-free knockoffs (MFK) procedure, which has been theoretically justified to control FDR in arbitrary models and arbitrary dimensions. We will also theoretically justify the robustness of MFK with respect to the misspecification of covariate distribution. These studies will lay the foundations for our developments in other aims. In Aim 2, we will develop deep learning approaches to predict viral contigs with higher accuracy, integrate our new algorithm with MFK to achieve FDR control for virus motif discovery, and investigate the power and robustness of our new procedure. In Aim 3, we will take into account the virus-host motif interactions and adapt our algorithms and theories in Aim 2 for predicting virus-host infectious interaction status. In Aim 4, we will apply the developed methods from the first three aims to analyze the shotgun metagenomics data sets in ExperimentHub to identify viruses and virus-host interactions associated with several diseases at some target FDR level. Both the algorithms and results will be disseminated through the web. The results from this study will be important for metagenomics studies under a variety of environments.

如今，大数据在现代科学研究的各个领域无处不在。许多当代应用，