Systems and methods that utilize machine learning algorithms to facilitate assembly of aids vaccine cocktails
US8478535B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 30, 2005 |
| Grant date | Jul 2, 2013 |
| Priority date | — |
| Expiry date | Jun 26, 2030 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG16B20/00
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The subject invention provides systems and methods that facilitate AIDS vaccine cocktail assembly via machine learning algorithms such as a cost function, a greedy algorithm, an expectation-maximization (EM) algorithm, etc. Such assembly can be utilized to generate vaccine cocktails for species of pathogens that evolve quickly under immune pressure of the host. For example, the systems and methods of the subject invention can be utilized to facilitate design of T cell vaccines for pathogens such HIV. In addition, the systems and methods of the subject invention can be utilized in connection with other applications, such as, for example, sequence alignment, motif discovery, classification, and recombination hot spot detection. The novel techniques described herein can provide for improvements over traditional approaches to designing vaccines by constructing vaccine cocktails with higher epitope coverage, for example, in comparison with cocktails of consensi, tree nodes and random strains from data.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.