title: Multilevel ensemble model for prediction of IgA and IgG antibodies.
abstract: Identification of antigen for inducing specific class of antibody is prime objective in peptide based vaccine designs, immunodiagnosis, and antibody productions. It's urge to introduce a reliable system with high accuracy and efficiency for prediction. In the present study, a novel multilevel ensemble model is developed for prediction of antibodies IgG and IgA. Epitope length is important in training the model and it is efficient to use variable length of epitopes. In this ensemble approach, seven different machine learning models are combined to predict variable length of epitopes (4 to 50). The proposed model of IgG specific epitopes achieves 94.43% of accuracy and IgA specific epitopes achieves 97.56% of accuracy with repeated 10-fold cross validation. The proposed model is compared with the existing system i.e. IgPred model and outcome of proposed model is improved.
authors.affiliation: Computer Science and Engineering Department, Thapar University, Patiala, Punjab 147004, India. Electronic address: divyakhanna1991@gmail.com.
authors.forename: Divya
authors.initials: D
authors.lastname: Khanna
authors.affiliation: Computer Science and Engineering Department, Thapar University, Patiala, Punjab 147004, India.
authors.forename: Prashant Singh
authors.initials: PS
authors.lastname: Rana
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D019540:Area Under Curve; D000939:Epitopes / Q000737:chemistry / Q000276:immunology; D006801:Humans; D007070:Immunoglobulin A / Q000737:chemistry* / Q000276:immunology*; D007074:Immunoglobulin G / Q000737:chemistry* / Q000276:immunology*; D000069550:Machine Learning; D008958:Models, Molecular; D011485:Protein Binding; D015203:Reproducibility of Results
chemical_list: D000939:Epitopes; D007070:Immunoglobulin A; D007074:Immunoglobulin G
keywords: Antibody; B-cell epitope; Machine learning models; Multilevel ensemble model; Regularized trees