IPPF-FE: an integrated peptide and protein function prediction framework based on fused features and ensemble models.
Brief Bioinform
; 24(1)2023 01 19.
Article
em En
| MEDLINE
| ID: mdl-36403184
ABSTRACT
The prediction of peptide and protein function is important for research and industrial applications, and many machine learning methods have been developed for this purpose. The existing models have encountered many challenges, including the lack of effective and comprehensive features and the limited applicability of each model. Here, we introduce an Integrated Peptide and Protein function prediction Framework based on Fused features and Ensemble models (IPPF-FE), which can accurately capture the relationship between features and labels. The results indicated that IPPF-FE outperformed existing state-of-the-art (SOTA) models on more than 8 different categories of peptide and protein tasks. In addition, t-distributed Stochastic Neighbour Embedding demonstrated the advantages of IPPF-FE. We anticipate that our method will become a versatile tool for peptide and protein prediction tasks and shed light on the future development of related models. The model is open source and available in the GitHub repository https//github.com/Luo-SynBioLab/IPPF-FE.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Proteínas
/
Federação Internacional de Planejamento Familiar
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Revista:
Brief Bioinform
Assunto da revista:
BIOLOGIA
/
INFORMATICA MEDICA
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
China