用张量补全技术修复毒理基因组数据库，提升预测精度。
Completion of the DrugMatrix Toxicogenomics Database using 3-Dimensional Tensors
- 保留组织、处理、转录组的三维结构，用张量方法补全数据。
- 相比传统方法，均方误差和绝对误差更低，更贴近原始分布。
- 可发现组织间关联，适用于跨物种药物研究。
我们探索使用张量补全方法完善DrugMatrix毒理基因组学数据集。假设通过保持数据的三维结构（组织、处理、转录组测量），并结合机器学习框架，能优于现有最先进方法。结果表明，新张量方法更准确地反映原始数据分布，有效捕捉器官特异性差异。相比传统的CP分解和二维矩阵分解方法，该方法在均方误差和平均绝对误差上均更低。此外，非负张量补全实现了组织间关系的揭示。研究不仅以更高精度填补了全球最大的活体毒理基因组数据库，也为未来跨物种药物研究（如从大鼠到人类）提供了有前景的方法支持。
原文摘要 · Abstract (English)
We explore applying a tensor completion approach to complete the DrugMatrix toxicogenomics dataset. Our hypothesis is that by preserving the 3-dimensional structure of the data, which comprises tissue, treatment, and transcriptomic measurements, and by leveraging a machine learning formulation, our approach will improve upon prior state-of-the-art results. Our results demonstrate that the new tensor-based method more accurately reflects the original data distribution and effectively captures organ-specific variability. The proposed tensor-based methodology achieved lower mean squared errors and mean absolute errors compared to both conventional Canonical Polyadic decomposition and 2-dimensional matrix factorization methods. In addition, our non-negative tensor completion implementation reveals relationships among tissues. Our findings not only complete the world's largest in-vivo toxicogenomics database with improved accuracy but also offer a promising methodology for future studies of drugs that may cross species barriers, for example, from rats to humans.
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