Predicting amyloid proteins using attention-based long short-term memory
Alzheimer’s disease (AD) is one of the genetically inherited neurodegenerative disorders that mostly occur when people get old. It can be recognized by severe memory impairment in the late stage, affecting cognitive function and general daily living. Reliable evidence confirms that the enhanced symp...
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PeerJ Inc.
2025-02-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-2660.pdf |
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| author | Zhuowen Li |
| author_facet | Zhuowen Li |
| author_sort | Zhuowen Li |
| collection | DOAJ |
| description | Alzheimer’s disease (AD) is one of the genetically inherited neurodegenerative disorders that mostly occur when people get old. It can be recognized by severe memory impairment in the late stage, affecting cognitive function and general daily living. Reliable evidence confirms that the enhanced symptoms of AD are linked to the accumulation of amyloid proteins. The dense population of amyloid proteins forms insoluble fibrillar structures, causing significant pathological impacts in various tissues. Understanding amyloid protein’s mechanisms and identifying them at an early stage plays an essential role in treating AD as well as prevalent amyloid-related diseases. Recently, although several machine learning methods proposed for amyloid protein identification have shown promising results, most of them have not yet fully exploited the sequence information of the amyloid proteins. In this study, we develop a computational model for in silico identification of amyloid proteins using bidirectional long short-term memory in combination with an attention mechanism. In the testing phase, our findings showed that the model developed by our proposed method outperformed those developed by state-of-the-art methods with an area under the receiver operating characteristic curve of 0.9126. |
| format | Article |
| id | doaj-art-d136d677cd104d3e94c38c3dae7755cd |
| institution | Kabale University |
| issn | 2376-5992 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj-art-d136d677cd104d3e94c38c3dae7755cd2025-02-09T15:05:08ZengPeerJ Inc.PeerJ Computer Science2376-59922025-02-0111e266010.7717/peerj-cs.2660Predicting amyloid proteins using attention-based long short-term memoryZhuowen LiAlzheimer’s disease (AD) is one of the genetically inherited neurodegenerative disorders that mostly occur when people get old. It can be recognized by severe memory impairment in the late stage, affecting cognitive function and general daily living. Reliable evidence confirms that the enhanced symptoms of AD are linked to the accumulation of amyloid proteins. The dense population of amyloid proteins forms insoluble fibrillar structures, causing significant pathological impacts in various tissues. Understanding amyloid protein’s mechanisms and identifying them at an early stage plays an essential role in treating AD as well as prevalent amyloid-related diseases. Recently, although several machine learning methods proposed for amyloid protein identification have shown promising results, most of them have not yet fully exploited the sequence information of the amyloid proteins. In this study, we develop a computational model for in silico identification of amyloid proteins using bidirectional long short-term memory in combination with an attention mechanism. In the testing phase, our findings showed that the model developed by our proposed method outperformed those developed by state-of-the-art methods with an area under the receiver operating characteristic curve of 0.9126.https://peerj.com/articles/cs-2660.pdfAmyloidTransformersDeep learningLSTMAlzheimerAttention |
| spellingShingle | Zhuowen Li Predicting amyloid proteins using attention-based long short-term memory PeerJ Computer Science Amyloid Transformers Deep learning LSTM Alzheimer Attention |
| title | Predicting amyloid proteins using attention-based long short-term memory |
| title_full | Predicting amyloid proteins using attention-based long short-term memory |
| title_fullStr | Predicting amyloid proteins using attention-based long short-term memory |
| title_full_unstemmed | Predicting amyloid proteins using attention-based long short-term memory |
| title_short | Predicting amyloid proteins using attention-based long short-term memory |
| title_sort | predicting amyloid proteins using attention based long short term memory |
| topic | Amyloid Transformers Deep learning LSTM Alzheimer Attention |
| url | https://peerj.com/articles/cs-2660.pdf |
| work_keys_str_mv | AT zhuowenli predictingamyloidproteinsusingattentionbasedlongshorttermmemory |