Open Access. Powered by Scholars. Published by Universities.®
- Discipline
-
- Applied Mathematics (1)
- Applied Statistics (1)
- Biochemistry, Biophysics, and Structural Biology (1)
- Bioinformatics (1)
- Biometry (1)
-
- Biostatistics (1)
- Biotechnology (1)
- Cancer Biology (1)
- Cell and Developmental Biology (1)
- Computational Biology (1)
- Computer Sciences (1)
- Genetics and Genomics (1)
- Genomics (1)
- Life Sciences (1)
- Longitudinal Data Analysis and Time Series (1)
- Medical Biomathematics and Biometrics (1)
- Medical Molecular Biology (1)
- Medical Sciences (1)
- Medicine and Health Sciences (1)
- Microarrays (1)
- Molecular Biology (1)
- Multivariate Analysis (1)
- Statistical Methodology (1)
- Statistical Models (1)
Articles 1 - 2 of 2
Full-Text Articles in Statistics and Probability
Integrative Bayesian Analysis Of High-Dimensional Multi-Platform Genomics Data, Wenting Wang, Veerabhadran Baladandayuthapani, Jeffrey S. Morris, Bradley M. Broom, Ganiraju C. Manyam, Kim-Anh Do
Integrative Bayesian Analysis Of High-Dimensional Multi-Platform Genomics Data, Wenting Wang, Veerabhadran Baladandayuthapani, Jeffrey S. Morris, Bradley M. Broom, Ganiraju C. Manyam, Kim-Anh Do
Jeffrey S. Morris
Motivation: Analyzing data from multi-platform genomics experiments combined with patients’ clinical outcomes helps us understand the complex biological processes that characterize a disease, as well as how these processes relate to the development of the disease. Current integration approaches that treat the data are limited in that they do not consider the fundamental biological relationships that exist among the data from platforms.
Statistical Model: We propose an integrative Bayesian analysis of genomics data (iBAG) framework for identifying important genes/biomarkers that are associated with clinical outcome. This framework uses a hierarchical modeling technique to combine the data obtained from multiple platforms …
Wavelet-Based Functional Mixed Model Analysis: Computational Considerations, Richard C. Herrick, Jeffrey S. Morris
Wavelet-Based Functional Mixed Model Analysis: Computational Considerations, Richard C. Herrick, Jeffrey S. Morris
Jeffrey S. Morris
Wavelet-based Functional Mixed Models is a new Bayesian method extending mixed models to irregular functional data (Morris and Carroll, JRSS-B, 2006). These data sets are typically very large and can quickly run into memory and time constraints unless these issues are carefully dealt with in the software. We reduce runtime by 1.) identifying and optimizing hotspots, 2.) using wavelet compression to do less computation with minimal impact on results, and 3.) dividing the code into multiple executables to be run in parallel using a grid computing resource. We discuss rules of thumb for estimating memory requirements and computation times in …