| DOI | Resolve DOI: https://doi.org/10.1109/ICMLA61862.2024.00112 |
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| Author | Search for: Jamali, Saeedeh Nasrin; Search for: Chaubey, Yogendra; Search for: Ebadi, Ashkan1ORCID identifier: https://orcid.org/0000-0002-4542-9105 |
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| Affiliation | - National Research Council Canada. Digital Technologies
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| Format | Text, Article |
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| Conference | 2024 International Conference on Machine Learning and Applications, ICMLA, December 18 - 20, 2024, Miami, Florida, United States |
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| Subject | protein family classification; deep learning; few-shot learning; siamese network; bioinformatics |
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| Abstract | Protein sequence analysis presents a significant challenge in bioinformatics, impacting applications such as disease investigation and precision medicine. Despite advancements in sequencing technologies and the resultant proliferation of databases, classifying protein families remains a hurdle. This study introduces a novel deep few-shot network specifically designed for protein family classification, addressing the limitations of traditional methods by utilizing deep learning with sparse training datasets. Our experiments show that this framework outperforms state-of-the-art baseline models, achieving 97.3 % precision and 95.2 % recall on the training set, and 95.0 % precision and 93.5 % recall on the test set, when trained with only 25 shots per class. This work represents the first endeavor in crafting a deep network tailored for primary sequence family classification, achieving remarkable performance with minimal observations. |
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| Publication date | 2024-12-18 |
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| Publisher | IEEE |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | 48fdc015-b516-455c-88c3-473445a0c4c3 |
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| Record created | 2025-03-12 |
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| Record modified | 2025-03-18 |
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