通过空间语义地形图在表格数据中发现可解释的模式

IF 26.8 1区 医学 Q1 ENGINEERING, BIOMEDICAL
Rui Yan, Md Tauhidual Islam, Lei Xing
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引用次数: 0

摘要

表格数据--样本的行和样本特征的列--在各学科中普遍使用。然而,表格表示法难以发现数据中的潜在关联,从而阻碍了对数据的分析和有用模式的发现。在这里,我们报告了一种广泛适用的策略,通过将每个数据样本重新配置为具有空间语义的二维地形图(我们称之为 TabMap),来揭示表格数据中错综复杂的关系。TabMap 将原始特征值保留为像素强度,并在地图中对特征之间的关系进行空间编码(两个相互关联的特征的强度与它们在地图上的距离相关)。TabMap 可以应用二维卷积神经网络来提取数据中的关联模式,以帮助数据分析,并根据重要性对特征进行排序,从而提供可解释性。我们将 TabMap 应用于 12 个数据集,展示了它在疾病诊断、人类活动识别、微生物鉴定和定量结构-活性关系分析等广泛生物医学应用领域的卓越预测性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Interpretable discovery of patterns in tabular data via spatially semantic topographic maps

Interpretable discovery of patterns in tabular data via spatially semantic topographic maps

Tabular data—rows of samples and columns of sample features—are ubiquitously used across disciplines. Yet the tabular representation makes it difficult to discover underlying associations in the data and thus hinders their analysis and the discovery of useful patterns. Here we report a broadly applicable strategy for unravelling intertwined relationships in tabular data by reconfiguring each data sample into a spatially semantic 2D topographic map, which we refer to as TabMap. A TabMap preserves the original feature values as pixel intensities, with the relationships among the features spatially encoded in the map (the strength of two inter-related features correlates with their distance on the map). TabMap makes it possible to apply 2D convolutional neural networks to extract association patterns in the data to aid data analysis, and offers interpretability by ranking features according to importance. We show the superior predictive performance of TabMap by applying it to 12 datasets across a wide range of biomedical applications, including disease diagnosis, human activity recognition, microbial identification and the analysis of quantitative structure–activity relationships.

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来源期刊
Nature Biomedical Engineering
Nature Biomedical Engineering Medicine-Medicine (miscellaneous)
CiteScore
45.30
自引率
1.10%
发文量
138
期刊介绍: Nature Biomedical Engineering is an online-only monthly journal that was launched in January 2017. It aims to publish original research, reviews, and commentary focusing on applied biomedicine and health technology. The journal targets a diverse audience, including life scientists who are involved in developing experimental or computational systems and methods to enhance our understanding of human physiology. It also covers biomedical researchers and engineers who are engaged in designing or optimizing therapies, assays, devices, or procedures for diagnosing or treating diseases. Additionally, clinicians, who make use of research outputs to evaluate patient health or administer therapy in various clinical settings and healthcare contexts, are also part of the target audience.
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