发表机构
Idaho State University; Massachusetts Institute of Technology; University of Michigan; Statistics Online Computational Resource (SOCR)（爱达荷州立大学; 麻省理工学院; 密歇根大学; 统计在线计算资源（SOCR））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出耦合张量-张量补全（CTTC）方法，融入张量形式辅助信息，在DTD和LINCS数据集上，其运行时间与RSE精度均优于HaLRTC等现有方法，可更准确预测药物效应。
AI 中文摘要
许多生物医学挑战可被建模为张量补全问题，即利用多维数组（张量）的观测条目来补全缺失值。在这类场景中，融入张量模式的辅助信息（如基因-基因相似性）可显著提升补全问题的求解效果。现有多数张量补全方法仅能融入矩阵形式的辅助信息，本研究提出一种可融入张量形式辅助信息的新型框架，即耦合张量-张量补全（CTTC）方法，该方法利用多模态张量间的隐藏关联来提升张量补全性能。除实际应用价值外，CTTC具有距离度量学习和群论的理论基础，我们推导了求解CTTC优化问题的交替算法，并证明其收敛至平稳点。最后，我们在两个基准数据集DTD和LINCS上对比了CTTC与HaLRTC、CTRC、Cell、NTDDR等现有张量补全方法的表现，结果显示CTTC在运行时间和RSE张量补全精度上均优于这些最先进的方法，能更准确地预测药物效应。
英文摘要
Many biomedical challenges can be posed as tensor completion problems where the observed entries of a multidimensional array (a tensor) are used to impute the missing values. In such settings, incorporating side information about the modes of the tensor, such as gene-gene similarity, can significantly enhance the solutions of the completion problem. Most existing tensor completion methods can only incorporate side information in the form of matrices. In this study, we introduce a novel framework to incorporate side information in the form of tensors. Our new approach, called Coupled Tensor-Tensor Completion (CTTC), leverages the hidden connections among multimodal tensors to improve tensor completion performance. In addition to practical utility, CTTC has theoretical foundations in distance metric learning and group theory. We derive an alternating algorithm to solve the CTTC optimization problem and establish its convergence to a stationary point. Finally, we show that CTTC outperforms state-of-the-art tensor completion methods at predicting drug effects. Results: Compared with other tensor completion methods, including HaLRTC, CTRC, Cell, and NTDDR, CTTC demonstrates superior run-time and RSE tensor completion accuracy on two benchmark datasets, DTD and LINCS.
