iAIPs: Identifying Anti-Inflammatory Peptides Using Random Forest.
anti-inflammatory peptides
evolutionary analysis
evolutionary information
feature extraction
random forest
Journal
Frontiers in genetics
ISSN: 1664-8021
Titre abrégé: Front Genet
Pays: Switzerland
ID NLM: 101560621
Informations de publication
Date de publication:
2021
2021
Historique:
received:
09
09
2021
accepted:
08
10
2021
entrez:
17
12
2021
pubmed:
18
12
2021
medline:
18
12
2021
Statut:
epublish
Résumé
Recently, several anti-inflammatory peptides (AIPs) have been found in the process of the inflammatory response, and these peptides have been used to treat some inflammatory and autoimmune diseases. Therefore, identifying AIPs accurately from a given amino acid sequences is critical for the discovery of novel and efficient anti-inflammatory peptide-based therapeutics and the acceleration of their application in therapy. In this paper, a random forest-based model called iAIPs for identifying AIPs is proposed. First, the original samples were encoded with three feature extraction methods, including g-gap dipeptide composition (GDC), dipeptide deviation from the expected mean (DDE), and amino acid composition (AAC). Second, the optimal feature subset is generated by a two-step feature selection method, in which the feature is ranked by the analysis of variance (ANOVA) method, and the optimal feature subset is generated by the incremental feature selection strategy. Finally, the optimal feature subset is inputted into the random forest classifier, and the identification model is constructed. Experiment results showed that iAIPs achieved an AUC value of 0.822 on an independent test dataset, which indicated that our proposed model has better performance than the existing methods. Furthermore, the extraction of features for peptide sequences provides the basis for evolutionary analysis. The study of peptide identification is helpful to understand the diversity of species and analyze the evolutionary history of species.
Identifiants
pubmed: 34917130
doi: 10.3389/fgene.2021.773202
pii: 773202
pmc: PMC8669811
doi:
Types de publication
Journal Article
Langues
eng
Pagination
773202Informations de copyright
Copyright © 2021 Zhao, Teng, Li and Chen.
Déclaration de conflit d'intérêts
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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