MultiFeatVotPIP: a voting-based ensemble learning framework for predicting proinflammatory peptides.
| Autor: | Yan C; School of Computer Science and Technology, Hainan University, 58 Renmin Avenue, Meilan District, Haidian Campus, Haikou 570228, China., Geng A; School of Computer Science and Technology, Hainan University, 58 Renmin Avenue, Meilan District, Haidian Campus, Haikou 570228, China., Pan Z; International Business School, Hainan University, 58 Renmin Avenue, Meilan District, Haidian Campus, Haikou 570228, China., Zhang Z; School of Computer Science and Technology, Hainan University, 58 Renmin Avenue, Meilan District, Haidian Campus, Haikou 570228, China., Cui F; School of Computer Science and Technology, Hainan University, 58 Renmin Avenue, Meilan District, Haidian Campus, Haikou 570228, China. |
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| Jazyk: | angličtina |
| Zdroj: | Briefings in bioinformatics [Brief Bioinform] 2024 Sep 23; Vol. 25 (6). |
| DOI: | 10.1093/bib/bbae505 |
| Abstrakt: | Inflammatory responses may lead to tissue or organ damage, and proinflammatory peptides (PIPs) are signaling peptides that can induce such responses. Many diseases have been redefined as inflammatory diseases. To identify PIPs more efficiently, we expanded the dataset and designed an ensemble learning model with manually encoded features. Specifically, we adopted a more comprehensive feature encoding method and considered the actual impact of certain features to filter them. Identification and prediction of PIPs were performed using an ensemble learning model based on five different classifiers. The results show that the model's sensitivity, specificity, accuracy, and Matthews correlation coefficient are all higher than those of the state-of-the-art models. We named this model MultiFeatVotPIP, and both the model and the data can be accessed publicly at https://github.com/ChaoruiYan019/MultiFeatVotPIP. Additionally, we have developed a user-friendly web interface for users, which can be accessed at http://www.bioai-lab.com/MultiFeatVotPIP. (© The Author(s) 2024. Published by Oxford University Press.) |
| Databáze: | MEDLINE |
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