Compressive Sampling for Phenotype Classification
Abstract
Phenotype classification has become an increasingly important genomic research method for disease identification and treatment. Phenotype classification is the investigation into the genetic information concerned with locating biomarkers (features) in order to identify an observed effect. The primary challenge associated with phenotype classification is with analyzing the data due to the inherent high-dimensionality of DNA data. As a result, phenotype classification faces challenges with feature selection, and consequently, classification accuracy. This research developed a methodology to alleviate these challenges while improving classification performance. The methodology leverages concepts of compressive sampling, to arrive at a process that identifies features most relevant to the phenotype. Additionally, this research presents a probabilistic acceptance of the RIP and uses it to qualify data frames constructed by the proposed methodology. Overall, I found this methodology as a viable approach to dimension reduction and feature selection, which improved phenotype classification accuracy.
Document Details
- Document Type
- Technical Report
- Publication Date
- Aug 30, 2018
- Accession Number
- AD1063251
Entities
People
- Eric L. Brooks
Organizations
- Air Force Institute of Technology