Title :
Discovering statistical vulnerabilities in highly mutable viruses: A random matrix approach
Author :
Quadeer, A.A. ; Louie, Raymond H. Y. ; Shekhar, K. ; Chakraborty, Ajoy K. ; Hsing, I. ; McKay, Matthew R.
Author_Institution :
Dept. of Electron. & Comput. Eng., HKUST, Hong Kong, China
fDate :
June 29 2014-July 2 2014
Abstract :
The advancement in fast DNA sequencing technologies has opened up new opportunities to explore a diverse set of questions in biomedical research. In this paper, we review a general method which utilizes the available sequence data to determine potential weaknesses in highly mutable viruses, and which has shown promise in the design of vaccines. A key computational part of this method employs concepts from random matrix theory to obtain a robust estimate of a large covariance matrix. We apply this general method on hepatitis C virus as an example, and verify its usefulness by linking with the existing experimental and structural data.
Keywords :
biology computing; covariance matrices; data handling; microorganisms; molecular biophysics; statistical analysis; biomedical research; covariance matrix; fast DNA sequencing technologies; hepatitis C virus; highly mutable viruses; random matrix approach; random matrix theory; sequence data utilization; statistical vulnerabilities; vaccine design; Correlation; Covariance matrices; Eigenvalues and eigenfunctions; Immune system; Noise; Proteins; Vaccines; Random matrices; estimation; hepatitis C virus;
Conference_Titel :
Statistical Signal Processing (SSP), 2014 IEEE Workshop on
Conference_Location :
Gold Coast, VIC
DOI :
10.1109/SSP.2014.6884561
