title: Ensemble approaches for improving HLA class I-peptide binding prediction.
abstract: Accurately predicting peptides binding to major histocompatibility complex (MHC) I molecules is of great importance to immunologists for elucidating the underlying mechanism of immune recognition and facilitating the design of peptide-based vaccine. Various computational methods have been developed for MHC I-peptide binding prediction, and several of them are reported to achieve high accuracy in recent evaluation on benchmark datasets. For attending the machine learning in immunology competition (MLIC) in prediction of human leukocyte antigen (HLA)-binding peptides, we (FudanCS) have made use of ensemble approaches to further improve the prediction performance by integrating the outputs of several leading predictors. Two ensemble approaches, PM and AvgTanh, have been implemented for attending MLIC. AvgTanh and PM achieved the fourth and the seventh out of all 20 submissions in MLIC in terms of the average AUC. In addition, AvgTanh was awarded the winner in the category of HLA-A*0101 of 9-mer. Overall, the competition results validate the effectiveness of ensemble approaches.
authors.affiliation: School of Computer Science, Fudan University, Shanghai 200433, China.
authors.forename: Xihao
authors.initials: X
authors.lastname: Hu
authors.forename: Hiroshi
authors.initials: H
authors.lastname: Mamitsuka
authors.forename: Shanfeng
authors.initials: S
authors.lastname: Zhu
mesh_terms: D001185:Artificial Intelligence; D030562:Databases, Protein; D015788:HLA-A1 Antigen / Q000378:metabolism; D015395:Histocompatibility Antigens Class I / Q000378:metabolism*; D006801:Humans; D009842:Oligopeptides / Q000378:metabolism*
chemical_list: C506470:HLA-A*01:01 antigen; D015788:HLA-A1 Antigen; D015395:Histocompatibility Antigens Class I; D009842:Oligopeptides