Predicting peptides binding to MHC class II molecules using multi-objective evolutionary algorithms

doi:10.1186/1471-2105-8-459

BMC Bioinformatics

Volume 8

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Research article

Predicting peptides binding to MHC class II molecules using multi-objective evolutionary algorithms

Menaka Rajapakse¹^,3 , Bertil Schmidt² , Lin Feng³ and Vladimir Brusic⁴

¹Institute for Infocomm Research, 21 Heng Mui Keng Terrace, 119613 Singapore

²NICTA VRL, University of Melbourne, Parkville, 3010 Australia

³School of Computer Engineering, Nanyang Technological University, Block N4, Nanyang Avenue, 639798 Singapore

⁴Cancer Vaccine Center, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02115 USA

author email corresponding author email

BMC Bioinformatics 2007, 8:459doi:10.1186/1471-2105-8-459


Published:	22 November 2007

Additional files

Additional file 1:

MOEA derived matrices on I-A^g7dataset. The two PSSM derived by using MOEA self-discovery and guided-discovery approaches are given in the Additional file 1.

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Additional file 2:

Motif logos obtained for I-A^g7from MOEA derived matrices. Figure 1 and Figure 2 illustrate motif logos derived from the alignments obtained from the MOEA guided-discovery and self-discovery approaches. The web server [79] was used to generate the motif logos as described in [68].

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