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 | |
| 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 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- Random matrices
- estimation
- hepatitis C virus
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