Discovering statistical vulnerabilities in highly mutable viruses: A random matrix approach

A. A. Quadeer; R. H.Y. Louie; K. Shekhar; A. K. Chakraborty; I. Hsing; M. R. McKay

doi:10.1109/SSP.2014.6884561

Discovering statistical vulnerabilities in highly mutable viruses: A random matrix approach

A. A. Quadeer, R. H.Y. Louie, K. Shekhar, A. K. Chakraborty, I. Hsing, M. R. McKay

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

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.

Original language	English
Title of host publication	2014 IEEE Workshop on Statistical Signal Processing, SSP 2014
Publisher	IEEE Computer Society
Pages	5-8
Number of pages	4
ISBN (Print)	9781479949755
DOIs	https://doi.org/10.1109/SSP.2014.6884561
State	Published - 2014
Externally published	Yes
Event	2014 IEEE Workshop on Statistical Signal Processing, SSP 2014 - Gold Coast, QLD, Australia Duration: 29 Jun 2014 → 2 Jul 2014

Publication series

Name	IEEE Workshop on Statistical Signal Processing Proceedings

Conference

Conference	2014 IEEE Workshop on Statistical Signal Processing, SSP 2014
Country/Territory	Australia
City	Gold Coast, QLD
Period	29/06/14 → 2/07/14

Keywords

Random matrices
estimation
hepatitis C virus

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/SSP.2014.6884561

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Quadeer, A. A., Louie, R. H. Y., Shekhar, K., Chakraborty, A. K., Hsing, I., & McKay, M. R. (2014). Discovering statistical vulnerabilities in highly mutable viruses: A random matrix approach. In 2014 IEEE Workshop on Statistical Signal Processing, SSP 2014 (pp. 5-8). Article 6884561 (IEEE Workshop on Statistical Signal Processing Proceedings). IEEE Computer Society. https://doi.org/10.1109/SSP.2014.6884561

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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 = "Random matrices, estimation, hepatitis C virus",

author = "Quadeer, {A. A.} and Louie, {R. H.Y.} and K. Shekhar and Chakraborty, {A. K.} and I. Hsing and McKay, {M. R.}",

year = "2014",

doi = "10.1109/SSP.2014.6884561",

language = "אנגלית",

isbn = "9781479949755",

series = "IEEE Workshop on Statistical Signal Processing Proceedings",

publisher = "IEEE Computer Society",

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booktitle = "2014 IEEE Workshop on Statistical Signal Processing, SSP 2014",

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Quadeer, AA, Louie, RHY, Shekhar, K, Chakraborty, AK, Hsing, I & McKay, MR 2014, Discovering statistical vulnerabilities in highly mutable viruses: A random matrix approach. in 2014 IEEE Workshop on Statistical Signal Processing, SSP 2014., 6884561, IEEE Workshop on Statistical Signal Processing Proceedings, IEEE Computer Society, pp. 5-8, 2014 IEEE Workshop on Statistical Signal Processing, SSP 2014, Gold Coast, QLD, Australia, 29/06/14. https://doi.org/10.1109/SSP.2014.6884561

Discovering statistical vulnerabilities in highly mutable viruses: A random matrix approach. / Quadeer, A. A.; Louie, R. H.Y.; Shekhar, K. et al.
2014 IEEE Workshop on Statistical Signal Processing, SSP 2014. IEEE Computer Society, 2014. p. 5-8 6884561 (IEEE Workshop on Statistical Signal Processing Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Discovering statistical vulnerabilities in highly mutable viruses: A random matrix approach

Abstract

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Keywords

UN SDGs

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