{"id":3828,"date":"2026-04-25T22:57:02","date_gmt":"2026-04-25T14:57:02","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=3828"},"modified":"2026-05-11T01:22:15","modified_gmt":"2026-05-10T17:22:15","slug":"jase-202609-32-010","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202609-32-010","title":{"rendered":"IA-Transformer: Prediction and Classification of \u03b2-Lactamase Proteins Using Transformer Model with Integrated Attention Mechanism"},"content":{"rendered":"\n
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<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 32<\/a><\/p>\n<\/div>\n\n\n\n

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Yuankun Du1<\/sup>, Fengping Liu2<\/sup><\/i><\/a>, and Yi Hou1<\/sup><\/p>\n\n\n\n

1<\/sup>College of Big Data and Artificial Intelligence, Zhengzhou University of Science and Technology, Zhengzhou, Henan 450064, China<\/p>\n\n\n\n

2<\/sup>School of Information Engineering, Zhengzhou University of Science and Technology, Zhengzhou, Henan 450064, China<\/p>\n<\/div>\n\n\n\n

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Received:\u00a0\u00a0July 4, 2025
Accepted:\u00a0September 29, 2025
Publication Date:\u00a0April 25, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

\"\u4e0a\u50b3\u5716\u7247\"\n\n\n

ROC curves of the IA-Transformer model and comparison models for \u03b2-lactamase prediction <\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation:  BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202609_32.010<\/a>  <\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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\u03b2-Lactamase proteins are the primary mediators of bacterial resistance to \u03b2-Lactam antibiotics, posing a severe threat to global public health. Accurate prediction and classification of \u03b2-Lactamase proteins are crucial for the development of novel antibiotics and the formulation of clinical treatment strategies. Traditional machine learning methods for \u03b2-Lactamase analysis often rely on manual feature engineering, which fails to fully capture the complex sequence patterns and contextual information of proteins. To address this limitation, this study proposes a Transformer model integrated with a multi-head attention mechanism (IA-Transformer) for the prediction and classification of \u03b2-Lactamase proteins. The IA-Transformer model innovatively integrates three attention modules: sequence-wise self-attention, residue-wise attention, and channel-wise attention. The sequence-wise self-attention captures long-range dependencies between amino acid residues in the protein sequence; the residue-wise attention emphasizes key functional residues related to \u03b2-Lactam hydrolysis; and the channel-wise attention optimizes the feature representation of different sequence motifs. Experimental results show that the IA-Transformer model achieves an accuracy of 98.2%, a sensitivity of 97.8%, a specificity of 98.5%, and an F1-score of 98.0% in \u03b2-Lactamase prediction, outperforming traditional methods such as SVM, Random Forest, and single-attention Transformer by 3.5%\u22127.2%.<\/p>\n\n\n\n

Keywords: \u03b2-Lactamase; Transformer Model; Integrated Attention Mechanism; Protein Prediction; Protein Classification; Antibiotic Resistance<\/em><\/p>\n\n\n\n

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