Prediction Of Peptides Binding To MHC Class I Alleles By Partial Periodic Pattern Mining

Meydan C., Sezerman U., Otu H.

International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing, Shanghai, Çin, 3 - 05 Ağustos 2009, ss.315-316 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1109/ijcbs.2009.122
  • Basıldığı Şehir: Shanghai
  • Basıldığı Ülke: Çin
  • Sayfa Sayıları: ss.315-316
  • Anahtar Kelimeler: motif mining, periodic pattern mining, major histocompatibility complex, machine learning
  • Acıbadem Mehmet Ali Aydınlar Üniversitesi Adresli: Hayır

Özet

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 nonbinding 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.