Prediction of peptides binding to MHC class I alleles by partial periodic pattern mining
Meydan, Cem and Otu, Hasan and Sezerman, Uğur (2009) Prediction of peptides binding to MHC class I alleles by partial periodic pattern mining. In: 4th International Symposium on Health Informatics and Bioinformatics (HIBIT 09), Ankara, Türkiye
MHC (Major Histocompatibility Complex) is a key player in the immune response of an organism. It is important to be able to predict which antigenic peptides will bind to a spe-cific MHC allele and which will not, creating possibilities for controlling immune response and for the applications of immunotherapy. However a problem encountered in the computational binding prediction methods for MHC class I is the presence of bulges and loops in the peptides, changing the total length. Most machine learning methods in use to-day require the sequences to be of same length to success-fully mine the binding motifs. We propose the use of time-based data mining methods in motif mining to be able to mine motifs position-independently. Also, the information for both binding and non-binding peptides are used on the contrary to the other methods which only rely on binding peptides. The prediction results are between 70-80% for the tested alleles.
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