BPFun: a deep learning framework for bioactive peptide function prediction using multi-label strategy by transformer-driven and sequence rich intrinsic information

Bioactive peptides are beneficial or have physiological effects on the life activities of biological organisms. The functions of bioactive peptides are diverse, usually with one or more, so accurately detecting the multiple functions of multi-functional peptides is extremely important. Traditional e...

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Veröffentlicht in:	BMC bioinformatics Jg. 26; H. 1; S. 187 - 27
Hauptverfasser:	Zhu, Lun, Sun, Hao, Yang, Sen
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London BioMed Central 21.07.2025 BioMed Central Ltd Springer Nature B.V BMC
Schlagworte:	Accuracy Algorithms Amino Acid Sequence Amino acids Analysis Angiogenesis Antihypertensives Bioactive peptides Bioinformatics Biological activity Biomedical and Life Sciences Biomolecules Classification Computational Biology - methods Computational Biology/Bioinformatics Computational linguistics Computer Appl. in Life Sciences Convolutional neural network Data augmentation Datasets Deep Learning Electric transformers Functionals Identification Identification and classification Identification methods Language processing Life Sciences Machine learning Methods Microarrays Multi-label learning Natural language interfaces Neural networks Peptides Peptides - chemistry Peptides - pharmacology Performance evaluation Physiological aspects Physiological effects Predictions Recurrent neural network Support vector machines Transformer China Puerto Rico Taiwan Multi-label learning Transformer Recurrent neural network Bioactive peptides Convolutional neural network
ISSN:	1471-2105, 1471-2105
Online-Zugang:	Volltext
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