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The most effective way to prevent allergies is to find the causative allergen at the source and avoid re-exposure. However, most of the current computational methods used to identify allergens were based on homology or conventional machine learning methods, which were inefficient and still had room to be improved for the detection of allergens with low homology. In addition, few methods based on deep learning were reported, although deep learning has been successfully applied to several tasks in protein sequence analysis. In the present work, a deep neural network-based model, called DeepAlgPro, was proposed to identify allergens. We showed its great accuracy and applicability to large-scale forecasts by comparing it to other available tools. Additionally, we used ablation experiments to demonstrate the critical importance of the convolutional module in our model. Moreover, further analyses showed that epitope features contributed to model decision-making, thus improving the model\u2019s interpretability. Finally, we found that DeepAlgPro was capable of detecting potential new allergens. Overall, DeepAlgPro can serve as powerful software for identifying allergens.<\/jats:p>","DOI":"10.1093\/bib\/bbad246","type":"journal-article","created":{"date-parts":[[2023,6,29]],"date-time":"2023-06-29T23:33:47Z","timestamp":1688081627000},"source":"Crossref","is-referenced-by-count":24,"title":["DeepAlgPro: an interpretable deep neural network model for predicting allergenic proteins"],"prefix":"10.1093","volume":"24","author":[{"given":"Chun","family":"He","sequence":"first","affiliation":[{"name":"Zhejiang University State Key Laboratory of Rice Biology and Breeding & Ministry of Agricultural and Rural Affairs Key Laboratory of Molecular Biology of Crop Pathogens and Insects, Institute of Insect Sciences, , Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0203-0663","authenticated-orcid":false,"given":"Xinhai","family":"Ye","sequence":"additional","affiliation":[{"name":"Zhejiang University College of Computer 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Rice Biology and Breeding & Ministry of Agricultural and Rural Affairs Key Laboratory of Molecular Biology of Crop Pathogens and Insects, Institute of Insect Sciences, , Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Wei","sequence":"additional","affiliation":[{"name":"City University of Hong Kong Department of Computer Science, , Hong Kong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fei","family":"Wu","sequence":"additional","affiliation":[{"name":"Zhejiang University College of Computer Science and Technology, , Hangzhou, China"},{"name":"Zhejiang University Shanghai Institute for Advanced Study, , Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4937-8867","authenticated-orcid":false,"given":"Gongyin","family":"Ye","sequence":"additional","affiliation":[{"name":"Zhejiang University State Key Laboratory of Rice Biology and Breeding & Ministry of 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