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Heat shock proteins (HSPs) are crucial cellular stress proteins that react to environmental cues, ensuring the preservation of cellular functions. They also play pivotal roles in orchestrating the immune response and participating in processes associated with cancer. Consequently, the classification of HSPs holds immense significance in enhancing our understanding of their biological functions and in various diseases. However, the use of computational methods for identifying and classifying HSPs still faces challenges related to accuracy and interpretability. In this study, we introduced MulCNN-HSP, a novel deep learning model based on multi-scale convolutional neural networks, for identifying and classifying of HSPs. Comparative results showed that MulCNN-HSP outperforms or matches existing models in the identification and classification of HSPs. Furthermore, MulCNN-HSP can extract and analyze essential features for the prediction task, enhancing its interpretability. To facilitate its accessibility, we have made MulCNN-HSP available at We hope that MulCNN-HSP will contribute to advancing the study of HSPs and their roles in various biological processes and diseases. Copyright © 2023. Published by Elsevier B.V.


Guiyang Zhang, Mingrui Li, Qiang Tang, Fanbo Meng, Pengmian Feng, Wei Chen. MulCNN-HSP: A multi-scale convolutional neural networks-based deep learning method for classification of heat shock proteins. International journal of biological macromolecules. 2024 Feb;257(Pt 2):128802

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PMID: 38101670

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