Big Data Integration in Biomedical Studies

• Activity Number: 282
• Type: Invited
• Date/Time: Tuesday, August 11, 2015 : 8:30 AM to 10:20 AM
• Sponsor: ASA
• Author(s): Hongtu Zhu*
• Companies: The University of North Carolina at Chapel Hill

Abstract

With the rapid growth of modern technology, many large-scale biomedical studies have collected massive datasets with large volumes of complex information (e.g., imaging, genetics, or clinical) from increasingly large cohorts, while high-dimensional missing data are frequently encountered in various stages of the data collection process. Simultaneously extracting and integrating rich and diverse heterogeneous information from such big data in the presence of high-dimensional missing data is critical for making major advances important for diagnosis, prevention, and treatment of numerous complex disorders (e.g., Alzheimer's disease). However, such extraction and integration in big data represent major computational and theoretical challenges for existing statistical methods. In this talk, we review three imminent challenges faced by researchers in the analysis of big data: (CH1) carrying out genome-wide single-nucleotide polymorphism (SNP)/marker set analysis for multivariate imaging phenotypes; (CH2) carrying out voxel-wise genome-wide SNP/marker set analysis for functional imaging phe- notypes; and (CH3) integrating imaging, genetic, and clinical data both at baseline and longitudinally to predict time-to-event outcomes (e.g., time-to-disease onset).