基于耦合张量分解的知识引导模式发现
Knowledge-guided Pattern Discovery via Coupled Tensor Factorizations
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中文总结 AI 辅助
本文提出知识引导方法，通过耦合张量分解联合分析真实与模拟数据，在代谢组学数据实验中提升了模式发现性能，还揭示了数据与计算模型的潜在差异。
中文摘要 AI 辅助
为了理解人类代谢组或人脑等复杂系统，人们采用不同传感技术生成复杂数据，这些数据集通常是多向的，即具有两个以上的变化轴，例如受试者-代谢物-时间数组。张量分解已成功从这类复杂数据中揭示可解释模式，但迄今为止主要是数据驱动的。另一方面，数据之外还有计算模型（关于这些系统的），它们是先验信息的丰富来源。本文提出一种知识引导方法，通过结合线性耦合的耦合张量分解，联合分析真实数据和使用计算模型生成的模拟数据，将数据与计算模型结合起来。我们在真实代谢组学测量上的实验表明，用模拟数据引导这类噪声数据的分析，可提升模式发现性能，同时还能揭示数据与计算模型之间的潜在差异。
英文摘要
In order to understand complex systems such as the human metabolome or human brain, different sensing technologies are used, generating complex data. These datasets are often multiway, i.e., with more than two axes of variation such as a subjects by metabolites by time array. While tensor factorizations have successfully revealed interpretable patterns from such complex data, they have so far been mainly data-driven. On the other hand, there is more to data -- there are computational models (of these systems), which are rich sources of prior information. In this paper, we introduce a knowledge-guided approach that brings together data and computational models by jointly analyzing real data and simulated data (generated using a computational model) using coupled tensor factorizations with linear coupling. Our experiments on real metabolomics measurements demonstrate that guiding the analysis of such noisy data with simulated data improves the pattern discovery performance while also revealing potential discrepancies between data and computational models.
