Novel dual gland GAN architecture improves human protein localization classification using salivary and pituitary gland inspired loss functions
Abstract Cellular classification is essential for understanding biological processes and disease mechanisms. This paper introduces a novel approach that employs two complementary loss functions within a Generative Adversarial Network (GAN) framework for processing images from the Human Protein Atlas...
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Nature Portfolio
2025-08-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-11254-w |
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| author | Hanaa Salem Marie Moatasem M. Draz Waleed Abd Elkhalik Mostafa Elbaz |
| author_facet | Hanaa Salem Marie Moatasem M. Draz Waleed Abd Elkhalik Mostafa Elbaz |
| author_sort | Hanaa Salem Marie |
| collection | DOAJ |
| description | Abstract Cellular classification is essential for understanding biological processes and disease mechanisms. This paper introduces a novel approach that employs two complementary loss functions within a Generative Adversarial Network (GAN) framework for processing images from the Human Protein Atlas dataset. Our method introduces the “Salivary Gland” loss function (SG-Loss), which addresses missing pixel imputation through a unique computational mechanism that models the graded secretion patterns of acinar cells, incorporating multi-scale contextual information to reconstruct incomplete cellular features. This is paired with our innovative “Pituitary Gland” loss function (PG-Loss), which preserves structural integrity through a novel homeostatic regularization approach that adaptively weights pixel relationships based on subcellular compartment boundaries, unlike conventional smoothing techniques. The SG-Loss specifically targets discontinuities in protein expression patterns, while PG-Loss maintains biological plausibility by enforcing organelle-specific constraints learned from annotated training data. Our proposed Dual-Gland GAN demonstrates superior performance with an Inception Score of 9.83 (± 0.31) and MS-SSIM Diversity of 0.187 (± 0.021). The model achieves impressive precision and recall metrics (0.872 and 0.835, respectively), resulting in an F1-score of 0.853. Training stability is reflected in minimal generator and discriminator loss variance (0.028 and 0.032) with convergence achieved in 78 epochs. Comprehensive evaluation shows high quality and diversity scores (0.912 and 0.894), yielding a combined score of 0.903, demonstrating the effectiveness of our biologically inspired approach for cellular image generation and classification. The results also prove the efficiency of the architecture in enhancing the classification results. |
| format | Article |
| id | doaj-art-99c5d25b7ed5485da3221b19e7c96caa |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-99c5d25b7ed5485da3221b19e7c96caa2025-08-20T03:46:01ZengNature PortfolioScientific Reports2045-23222025-08-0115114110.1038/s41598-025-11254-wNovel dual gland GAN architecture improves human protein localization classification using salivary and pituitary gland inspired loss functionsHanaa Salem Marie0Moatasem M. Draz1Waleed Abd Elkhalik2Mostafa Elbaz3Faculty of Artificial Intelligence, Delta University for Science and TechnologyInformation Systems Department, Faculty of Computers and Information, Kafrelsheikh UniversityLecturer at Faculty of Computers and Informatics, Tanta UniversityDepartment of Computer Science, Faculty of Computers and Informatics, Kafrelsheikh UniversityAbstract Cellular classification is essential for understanding biological processes and disease mechanisms. This paper introduces a novel approach that employs two complementary loss functions within a Generative Adversarial Network (GAN) framework for processing images from the Human Protein Atlas dataset. Our method introduces the “Salivary Gland” loss function (SG-Loss), which addresses missing pixel imputation through a unique computational mechanism that models the graded secretion patterns of acinar cells, incorporating multi-scale contextual information to reconstruct incomplete cellular features. This is paired with our innovative “Pituitary Gland” loss function (PG-Loss), which preserves structural integrity through a novel homeostatic regularization approach that adaptively weights pixel relationships based on subcellular compartment boundaries, unlike conventional smoothing techniques. The SG-Loss specifically targets discontinuities in protein expression patterns, while PG-Loss maintains biological plausibility by enforcing organelle-specific constraints learned from annotated training data. Our proposed Dual-Gland GAN demonstrates superior performance with an Inception Score of 9.83 (± 0.31) and MS-SSIM Diversity of 0.187 (± 0.021). The model achieves impressive precision and recall metrics (0.872 and 0.835, respectively), resulting in an F1-score of 0.853. Training stability is reflected in minimal generator and discriminator loss variance (0.028 and 0.032) with convergence achieved in 78 epochs. Comprehensive evaluation shows high quality and diversity scores (0.912 and 0.894), yielding a combined score of 0.903, demonstrating the effectiveness of our biologically inspired approach for cellular image generation and classification. The results also prove the efficiency of the architecture in enhancing the classification results.https://doi.org/10.1038/s41598-025-11254-wCellular classificationHuman protein atlasBioimage informaticsProtein localizationCell phenotyping |
| spellingShingle | Hanaa Salem Marie Moatasem M. Draz Waleed Abd Elkhalik Mostafa Elbaz Novel dual gland GAN architecture improves human protein localization classification using salivary and pituitary gland inspired loss functions Scientific Reports Cellular classification Human protein atlas Bioimage informatics Protein localization Cell phenotyping |
| title | Novel dual gland GAN architecture improves human protein localization classification using salivary and pituitary gland inspired loss functions |
| title_full | Novel dual gland GAN architecture improves human protein localization classification using salivary and pituitary gland inspired loss functions |
| title_fullStr | Novel dual gland GAN architecture improves human protein localization classification using salivary and pituitary gland inspired loss functions |
| title_full_unstemmed | Novel dual gland GAN architecture improves human protein localization classification using salivary and pituitary gland inspired loss functions |
| title_short | Novel dual gland GAN architecture improves human protein localization classification using salivary and pituitary gland inspired loss functions |
| title_sort | novel dual gland gan architecture improves human protein localization classification using salivary and pituitary gland inspired loss functions |
| topic | Cellular classification Human protein atlas Bioimage informatics Protein localization Cell phenotyping |
| url | https://doi.org/10.1038/s41598-025-11254-w |
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