Sequence homology score-based deep fuzzy network for identifying therapeutic peptides.
Neural Netw
; 178: 106458, 2024 Oct.
Article
en En
| MEDLINE
| ID: mdl-38901093
ABSTRACT
The detection of therapeutic peptides is a topic of immense interest in the biomedical field. Conventional biochemical experiment-based detection techniques are tedious and time-consuming. Computational biology has become a useful tool for improving the detection efficiency of therapeutic peptides. Most computational methods do not consider the deviation caused by noise. To improve the generalization performance of therapeutic peptide prediction methods, this work presents a sequence homology score-based deep fuzzy echo-state network with maximizing mixture correntropy (SHS-DFESN-MMC) model. Our method is compared with the existing methods on eight types of therapeutic peptide datasets. The model parameters are determined by 10 fold cross-validation on their training sets and verified by independent test sets. Across the 8 datasets, the average area under the receiver operating characteristic curve (AUC) values of SHS-DFESN-MMC are the highest on both the training (0.926) and independent sets (0.923).
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MEDLINE
Asunto principal:
Péptidos
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Redes Neurales de la Computación
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Lógica Difusa
Idioma:
En
Revista:
Neural Netw
Asunto de la revista:
NEUROLOGIA
Año:
2024
Tipo del documento:
Article