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
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Zusammenfassung: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 experimental identification methods are time-consuming, laborious and costly. To overcome these problems, we adopt a computational biology approach and propose a new model BPFun based on deep learning, which can predict seven functions including anticancer, antibacterial, antihypertensive and so on. In BPFun, we obtained the features of bioactive peptides from different aspects, including biological and physicochemical features. Meanwhile, adopting data augmentation to solve the problem of data imbalance. We combine convolutional networks of different scales and Bi-LSTM layers to obtain high-level feature vectors of different features. Finally, the prediction performance is improved by combining these fused features and combining the self-attention mechanism and the Bi-LSTM layer. Our experiments show that BPFun based on five types of sequence features significantly improves the prediction performance of bioactive peptides. Experiments on the test dataset showed that BPFun gets the accuracy and absolute truth value of 0.6577 and 0.6573 on the dataset of seven functional classifications and was superior to other methods. Codes and data are available at https://github.com/291357657/BPFun .
Bibliographie:ObjectType-Article-1
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ISSN:1471-2105
1471-2105
DOI:10.1186/s12859-025-06190-5