Stack-VTP: prediction of vesicle transport proteins based on stacked ensemble classifier and evolutionary information.
Ensemble learning
Protein prediction
Stacked model
Vesicle transport proteins
Journal
BMC bioinformatics
ISSN: 1471-2105
Titre abrégé: BMC Bioinformatics
Pays: England
ID NLM: 100965194
Informations de publication
Date de publication:
07 Apr 2023
07 Apr 2023
Historique:
received:
07
08
2022
accepted:
28
03
2023
medline:
11
4
2023
entrez:
7
4
2023
pubmed:
8
4
2023
Statut:
epublish
Résumé
Vesicle transport proteins not only play an important role in the transmembrane transport of molecules, but also have a place in the field of biomedicine, so the identification of vesicle transport proteins is particularly important. We propose a method based on ensemble learning and evolutionary information to identify vesicle transport proteins. Firstly, we preprocess the imbalanced dataset by random undersampling. Secondly, we extract position-specific scoring matrix (PSSM) from protein sequences, and then further extract AADP-PSSM and RPSSM features from PSSM, and use the Max-Relevance-Max-Distance (MRMD) algorithm to select the optimal feature subset. Finally, the optimal feature subset is fed into the stacked classifier for vesicle transport proteins identification. The experimental results show that the of accuracy (ACC), sensitivity (SN) and specificity (SP) of our method on the independent testing set are 82.53%, 0.774 and 0.836, respectively. The SN, SP and ACC of our proposed method are 0.013, 0.007 and 0.76% higher than the current state-of-the-art methods.
Identifiants
pubmed: 37029385
doi: 10.1186/s12859-023-05257-5
pii: 10.1186/s12859-023-05257-5
pmc: PMC10080812
doi:
Substances chimiques
Carrier Proteins
0
Vesicular Transport Proteins
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
137Subventions
Organisme : National Natural Science Foundation of China
ID : 62172087
Organisme : Fundamental Research Funds for the Central Universities
ID : 2572021BH01
Informations de copyright
© 2023. The Author(s).
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