Prediction of MHC class II-binding peptides based on sequential learning

• Published in: 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583) Vol. 4; pp. 3158 - 3163 vol.4
• Main Authors: An Zeng, Qi-Lun Zheng, Pan, D., Hong Peng
• Format: Conference Proceeding
• Language: English
• Published: Piscataway NJ IEEE 2004
• Subjects: Accuracy; Applied sciences; Artificial neural networks; Computer science; control theory; systems; Computer systems and distributed systems. User interface; Control theory. Systems; Exact sciences and technology; Hidden Markov models; Machine learning; Mobile communication; Network synthesis; Neurons; Peptides; Prediction methods; Predictive models; Software; Input output; Backpropagation algorithm; Software architecture; High precision; Masking; Neural network; Distributed computing; Modeling
• ISBN: 0780385667; 9780780385665
• ISSN: 1062-922X; 2577-1655
• DOI: 10.1109/ICSMC.2004.1400825

Predicting which peptides can bind to a specific major histocompatibility complex (MHC) molecule could have great value for minimizing the number of peptides required to be synthesized and assayed. Artificial neural networks (ANNs)-based prediction method, which usually adopts backpropagation neural networks (BPNN) as a prediction model has high prediction accuracy, while its learning efficiency is low and its incremental learning cannot be realized. Sequential learning (SL) adds output neurons in such a way that a correct mapping between input and output patterns is guaranteed. We propose the SL-based prediction method, which chooses the modified sequential learning ahead masking (SLAM) model combined with incremental learning (FIL-SLAM) to predict MHC II-binding peptides. For the experimental data composed of 650 peptides to bind or not bind to HLA-DR4 (B1*0401), compared with BPNN, the proposed method shows a significant reduction in consuming time (95%) with only a slight reduction (1.3%) in average prediction accuracy.