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DeePhafier: a phage lifestyle classifier using a multilayer self-attention neural network combining protein information.

ABSTRACT: Bacteriophages are the viruses that infect bacterial cells. They are the most diverse biological entities on earth and play important roles in microbiome. According to the phage lifestyle, phages can be divided into the virulent phages and the temperate phages. Classifying virulent and temperate phages is crucial for further understanding of the phage-host interactions. Although there are several methods designed for phage lifestyle classification, they merely either consider sequence features or gene features, leading to low accuracy. A new computational method, DeePhafier, is proposed to improve classification performance on phage lifestyle. Built by several multilayer self-attention neural networks, a global self-attention neural network, and being combined by protein features of the Position Specific Scoring Matrix matrix, DeePhafier improves the classification accuracy and outperforms two benchmark methods. The accuracy of DeePhafier on five-fold cross-validation is as high as 87.54% for sequences with length >2000bp.

SUBMITTER: Miao Y

PROVIDER: S-EPMC11304974 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature

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DeePhafier: a phage lifestyle classifier using a multilayer self-attention neural network combining protein information.

Miao Yan Y Sun Zhenyuan Z Lin Chen C Gu Haoran H Ma Chenjing C Liang Yingjian Y Wang Guohua G

Briefings in bioinformatics 20240701 5

Bacteriophages are the viruses that infect bacterial cells. They are the most diverse biological entities on earth and play important roles in microbiome. According to the phage lifestyle, phages can be divided into the virulent phages and the temperate phages. Classifying virulent and temperate phages is crucial for further understanding of the phage-host interactions. Although there are several methods designed for phage lifestyle classification, they merely either consider sequence features o ...[more]

PMID: 39110476

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Project description:BackgroundCervical cancer remains a critical global health issue, responsible for over 600,000 new cases and 300,000 deaths annually. Pathological imaging of cervical cancer is a crucial diagnostic tool. However, distinguishing specific areas of cellular differentiation remains challenging because of the lack of clear boundaries between cells at various stages of differentiation. To address the limitations of conventional clinical and deep learning (DL) methods, we developed a mobile attention classification network (MacNet) with multiscale features, aiming to increase the accuracy of differentiation classification and quantitatively analyze cervical cancer cell differentiation.MethodsWe investigated the application of MacNet for classifying non-background images into 3 stages of cervical cancer differentiation. The feature maps are processed through the Mobile Convolution Neural Network with Mobile Attention (MCMA) module, which integrates mobile convolutional blocks and mobile attention blocks. MacNet harnesses the benefits of the image pyramid structure and self-attention mechanism, enabling multiscale feature extraction and emulation of clinical pathologist analysis. The final prediction is generated by the adaptive fusion module, which aggregates features into a unified output.ResultsComparative evaluations demonstrated that MacNet outperforms existing models. The proposed method achieved the best classification accuracy of 92.34% among all 7 DL-based models. Specifically, the result achieved by MacNet was 2.62% greater than that of Inception Version 3, 7.9% greater than that of vision transformer, 8.08% greater than that of the visual geometry group network, 3.21% greater than that of Densely Connected Convolutional Network, 2.85% greater than that of shifted window transformer (Swin transformer), 5.4% greater than that of Cross Stage Partial DarkNet, and 5.41% greater than that of Residual Neural Network. At the same time, MacNet also achieved superior results in recall, precision, and F1 score.ConclusionsWe have proposed a lightweight neural network method that innovatively combines attention mechanisms with convolutional neural networks (CNNs) to efficiently utilize multiscale information from histopathological images. This integration enables the precise quantitative display of different stages of differentiation in cervical cancer. By doing so, our method not only enhances diagnostic accuracy but also provides clinicians with a more effective tool for faster and more reliable diagnosis, representing a significant advancement in the field of pathological imaging.

Dataset Information

DeePhafier: a phage lifestyle classifier using a multilayer self-attention neural network combining protein information.

Publications

DeePhafier: a phage lifestyle classifier using a multilayer self-attention neural network combining protein information.

Similar Datasets

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