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Full-Text Articles in Microarrays
Classification Of Coronary Artery Disease In Non-Diabetic Patients Using Artificial Neural Networks, Demond Handley
Classification Of Coronary Artery Disease In Non-Diabetic Patients Using Artificial Neural Networks, Demond Handley
Annual Symposium on Biomathematics and Ecology Education and Research
No abstract provided.
Unified Methods For Feature Selection In Large-Scale Genomic Studies With Censored Survival Outcomes, Lauren Spirko-Burns, Karthik Devarajan
Unified Methods For Feature Selection In Large-Scale Genomic Studies With Censored Survival Outcomes, Lauren Spirko-Burns, Karthik Devarajan
COBRA Preprint Series
One of the major goals in large-scale genomic studies is to identify genes with a prognostic impact on time-to-event outcomes which provide insight into the disease's process. With rapid developments in high-throughput genomic technologies in the past two decades, the scientific community is able to monitor the expression levels of tens of thousands of genes and proteins resulting in enormous data sets where the number of genomic features is far greater than the number of subjects. Methods based on univariate Cox regression are often used to select genomic features related to survival outcome; however, the Cox model assumes proportional hazards …
Estimation And Variable Selection In High-Dimensional Settings With Mismeasured Observations, Michael Byrd
Estimation And Variable Selection In High-Dimensional Settings With Mismeasured Observations, Michael Byrd
Statistical Science Theses and Dissertations
Understanding high-dimensional data has become essential for practitioners across many disciplines. The general increase in ability to collect large amounts of data has prompted statistical methods to adapt for the rising number of possible relationships to be uncovered. The key to this adaptation has been the notion of sparse models, or, rather, models where most relationships between variables are assumed to be negligible at best. Driving these sparse models have been constraints on the solution set, yielding regularization penalties imposed on the optimization procedure. While these penalties have found great success, they are typically formulated with strong assumptions on the …