Attention-based deep learning for accurate cell image analysis
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| Publicado en: | Scientific Reports (Nature Publisher Group) vol. 15, no. 1 (2025), p. 1265 |
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| Publicado: |
Nature Publishing Group
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| Acceso en línea: | Citation/Abstract Full Text - PDF |
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| 003 | UK-CbPIL | ||
| 022 | |a 2045-2322 | ||
| 024 | 7 | |a 10.1038/s41598-025-85608-9 |2 doi | |
| 035 | |a 3152801918 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 274855 |2 nlm | ||
| 245 | 1 | |a Attention-based deep learning for accurate cell image analysis | |
| 260 | |b Nature Publishing Group |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a High-content analysis (HCA) holds enormous potential for drug discovery and research, but widely used methods can be cumbersome and yield inaccurate results. Noisy and redundant signals in cell images impede accurate deep learning-based image analysis. To address these issues, we introduce X-Profiler, a novel HCA method that combines cellular experiments, image processing, and deep learning modeling. X-Profiler combines the convolutional neural network and Transformer to encode high-content images, effectively filtering out noisy signals and precisely characterizing cell phenotypes. In comparative tests on drug-induced cardiotoxicity, mitochondrial toxicity classification, and compound classification, X-Profiler outperformed both DeepProfiler and CellProfiler, as two highly recognized and representative methods in this field. Our results demonstrate the utility and versatility of X-Profiler, and we anticipate its wide application in HCA for advancing drug development and disease research. | |
| 653 | |a Image processing | ||
| 653 | |a Information processing | ||
| 653 | |a Cardiotoxicity | ||
| 653 | |a Drug development | ||
| 653 | |a Deep learning | ||
| 653 | |a Neural networks | ||
| 653 | |a Phenotypes | ||
| 653 | |a Big Data | ||
| 653 | |a Datasets | ||
| 653 | |a Toxicity | ||
| 653 | |a Data processing | ||
| 653 | |a Research & development--R&D | ||
| 653 | |a Genes | ||
| 653 | |a Morphology | ||
| 653 | |a Painting | ||
| 653 | |a Environmental | ||
| 773 | 0 | |t Scientific Reports (Nature Publisher Group) |g vol. 15, no. 1 (2025), p. 1265 | |
| 786 | 0 | |d ProQuest |t Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3152801918/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3152801918/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |