DeepMC-iNABP: Deep learning for multiclass identification and classification of nucleic acid-binding proteins.
DNA-binding protein
Deep learning
Multiclass classification
Nucleic acid-binding protein
RNA-binding protein
Journal
Computational and structural biotechnology journal
ISSN: 2001-0370
Titre abrégé: Comput Struct Biotechnol J
Pays: Netherlands
ID NLM: 101585369
Informations de publication
Date de publication:
2022
2022
Historique:
received:
08
02
2022
revised:
06
04
2022
accepted:
20
04
2022
entrez:
6
5
2022
pubmed:
7
5
2022
medline:
7
5
2022
Statut:
epublish
Résumé
Nucleic acid-binding proteins (NABPs), including DNA-binding proteins (DBPs) and RNA-binding proteins (RBPs), play vital roles in gene expression. Accurate identification of these proteins is crucial. However, there are two existing challenges: one is the problem of ignoring DNA- and RNA-binding proteins (DRBPs), and the other is a cross-predicting problem referring to DBP predictors predicting DBPs as RBPs, and vice versa. In this study, we proposed a computational predictor, called DeepMC-iNABP, with the goal of solving these difficulties by utilizing a multiclass classification strategy and deep learning approaches. DBPs, RBPs, DRBPs and non-NABPs as separate classes of data were used for training the DeepMC-iNABP model. The results on test data collected in this study and two independent test datasets showed that DeepMC-iNABP has a strong advantage in identifying the DRBPs and has the ability to alleviate the cross-prediction problem to a certain extent. The web-server of DeepMC-iNABP is freely available at http://www.deepmc-inabp.net/. The datasets used in this research can also be downloaded from the website.
Identifiants
pubmed: 35521556
doi: 10.1016/j.csbj.2022.04.029
pii: S2001-0370(22)00146-5
pmc: PMC9065708
doi:
Types de publication
Journal Article
Langues
eng
Pagination
2020-2028Informations de copyright
© 2022 The Author(s).
Déclaration de conflit d'intérêts
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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