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

fYear

2014

fDate

June 29 2014-July 2 2014

Firstpage

5

Lastpage

8

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Statistical Signal Processing (SSP), 2014 IEEE Workshop on

Conference_Location

Gold Coast, VIC

Type

conf

DOI

10.1109/SSP.2014.6884561

Filename

6884561