Open Access. Powered by Scholars. Published by Universities.®
- Discipline
-
- Applied Mathematics (1)
- Biochemical Phenomena, Metabolism, and Nutrition (1)
- Biochemistry (1)
- Biochemistry, Biophysics, and Structural Biology (1)
- Biological Phenomena, Cell Phenomena, and Immunity (1)
-
- Biology (1)
- Biotechnology (1)
- Cancer Biology (1)
- Cell and Developmental Biology (1)
- Genetic Processes (1)
- Genetic Structures (1)
- Genomics (1)
- Integrative Biology (1)
- Medical Biomathematics and Biometrics (1)
- Medical Biotechnology (1)
- Medical Genetics (1)
- Medical Molecular Biology (1)
- Medical Pathology (1)
- Medical Sciences (1)
- Medicine and Health Sciences (1)
- Microarrays (1)
- Molecular Genetics (1)
- Multivariate Analysis (1)
- Numerical Analysis and Computation (1)
- Other Genetics and Genomics (1)
Articles 1 - 2 of 2
Full-Text Articles in Genetics and Genomics
Supervised Dimension Reduction For Large-Scale "Omics" Data With Censored Survival Outcomes Under Possible Non-Proportional Hazards, Lauren Spirko-Burns, Karthik Devarajan
Supervised Dimension Reduction For Large-Scale "Omics" Data With Censored Survival Outcomes Under Possible Non-Proportional Hazards, Lauren Spirko-Burns, Karthik Devarajan
COBRA Preprint Series
The past two decades have witnessed significant advances in high-throughput ``omics" technologies such as genomics, proteomics, metabolomics, transcriptomics and radiomics. These technologies have enabled simultaneous measurement of the expression levels of tens of thousands of features from individual patient samples and have generated enormous amounts of data that require analysis and interpretation. One specific area of interest has been in studying the relationship between these features and patient outcomes, such as overall and recurrence-free survival, with the goal of developing a predictive ``omics" profile. Large-scale studies often suffer from the presence of a large fraction of censored observations and potential …
Finding Cancer Subtypes In Microarray Data Using Random Projections, Debashis Ghosh
Finding Cancer Subtypes In Microarray Data Using Random Projections, Debashis Ghosh
The University of Michigan Department of Biostatistics Working Paper Series
One of the benefits of profiling of cancer samples using microarrays is the generation of molecular fingerprints that will define subtypes of disease. Such subgroups have typically been found in microarray data using hierarchical clustering. A major problem in interpretation of the output is determining the number of clusters. We approach the problem of determining disease subtypes using mixture models. A novel estimation procedure of the parameters in the mixture model is developed based on a combination of random projections and the expectation-maximization algorithm. Because the approach is probabilistic, our approach provides a measure for the number of true clusters …