We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
NIgPred: Class-Specific Antibody Prediction for Linear B-Cell Epitopes Based on Heterogeneous Features and Machine-Learning Approaches.
- Authors
Tung, Chi-Hua; Chang, Yi-Sheng; Chang, Kai-Po; Chu, Yen-Wei
- Abstract
Upon invasion by foreign pathogens, specific antibodies can identify specific foreign antigens and disable them. As a result of this ability, antibodies can help with vaccine production and food allergen detection in patients. Many studies have focused on predicting linear B-cell epitopes, but only two prediction tools are currently available to predict the sub-type of an epitope. NIgPred was developed as a prediction tool for IgA, IgE, and IgG. NIgPred integrates various heterologous features with machine-learning approaches. Differently from previous studies, our study considered peptide-characteristic correlation and autocorrelation features. Sixty kinds of classifier were applied to construct the best prediction model. Furthermore, the genetic algorithm and hill-climbing algorithm were used to select the most suitable features for improving the accuracy and reducing the time complexity of the training model. NIgPred was found to be superior to the currently available tools for predicting IgE epitopes and IgG epitopes on independent test sets. Moreover, NIgPred achieved a prediction accuracy of 100% for the IgG epitopes of a coronavirus data set. NIgPred is publicly available at our website.
- Subjects
MILITARY invasion; IMMUNOGLOBULINS; PREDICTION models; FORECASTING; GENETIC algorithms; ANTIGENS; EPITOPES; IMMUNOGLOBULIN E
- Publication
Viruses (1999-4915), 2021, Vol 13, Issue 8, p1531
- ISSN
1999-4915
- Publication type
Article
- DOI
10.3390/v13081531