Prediction of peptides binding to MHC class I alleles by partial periodic pattern mining

Warning The system is temporarily closed to updates for reporting purpose.

Meydan, Cem and Sezerman, Uğur and Otu, Hasan (2009) Prediction of peptides binding to MHC class I alleles by partial periodic pattern mining. In: International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing, 2009 (IJCBS '09), Shangai, China

PDF (This is a RoMEO green publisher -- author can archive pre-print (ie pre-refereeing)) - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader

Official URL: http://dx.doi.org/10.1109/IJCBS.2009.122


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 specific MHC allele and which will not, creating possibilities for controlling immune response and for the applications of immunotherapy. However, a problem for MHC class I is the presence of bulges and loops in the peptides, changing the total length. Most machine learning methods in use today require the sequences to be of same length to successfully 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 is used on the contrary to the other methods which only rely on binding peptides. The prediction results are between 60-95% for the tested alleles.

Item Type:Papers in Conference Proceedings
Uncontrolled Keywords:machine learning; major histocompatibility complex; motif mining; periodic pattern mining
Subjects:Q Science > Q Science (General)
ID Code:13254
Deposited By:Uğur Sezerman
Deposited On:04 Dec 2009 10:38
Last Modified:24 Jul 2019 11:14

Repository Staff Only: item control page