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Simon Perkins

Publications and source records attributed to Simon Perkins.

2 recordsLinked to original sources
PubMed2006

Polyvalent vaccines for optimal coverage of potential T-cell epitopes in global HIV-1 variants.

HIV-1/AIDS vaccines must address the extreme diversity of HIV-1. We have designed new polyvalent vaccine antigens comprised of sets of 'mosaic' proteins, assembled from fragments of natural sequences via a computational optimization method. Mosaic proteins resemble natural proteins, and a mosaic set maximizes the coverage of potential T-cell epitopes (peptides of nine amino acids) for a viral population. We found that coverage of viral diversity using mosaics was greatly increased compared to coverage by natural-sequence vaccine candidates, for both variable and conserved proteins; for conserved HIV-1 proteins, global coverage may be feasible. For example, four mosaic proteins perfectly matched 74% of 9-amino-acid potential epitopes in global Gag sequences; 87% of potential epitopes matched at least 8 of 9 positions. In contrast, a single natural Gag protein covered only 37% (9 of 9) and 67% (8 of 9). Mosaics provide diversity coverage comparable to that afforded by thousands of separate peptides, but, because the fragments of natural proteins are compressed into a small number of native-like proteins, they are tractable for vaccines.

AIDS Vaccines↗
PubMed2003

Accurate on-line support vector regression.

Batch implementations of support vector regression (SVR) are inefficient when used in an on-line setting because they must be retrained from scratch every time the training set is modified. Following an incremental support vector classification algorithm introduced by Cauwenberghs and Poggio (2001), we have developed an accurate on-line support vector regression (AOSVR) that efficiently updates a trained SVR function whenever a sample is added to or removed from the training set. The updated SVR function is identical to that produced by a batch algorithm. Applications of AOSVR in both on-line and cross-validation scenarios are presented. In both scenarios, numerical experiments indicate that AOSVR is faster than batch SVR algorithms with both cold and warm start.

Algorithms↗