Spatial Perception for Structured and?Unstructured Data In topological Data Analysis
Spatial Perception for Structured and?Unstructured Data In topological Data Analysis
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拓扑数据分析中结构化和非结构化数据的空间感知
DOI：
10.1007/978-3-030-60104-1_12
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Kurihara Koji
中科院分区：
文献类型：
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作者：
Kitanishi Yoshitake;Ishioka Fumio;Iizuka Masaya;Kurihara Koji
近年来，积累了大量的数据和信息。对于数据的更新和增加，使用传统方法很难在空间上捕获这些数据的特征或稳健地可视化它们。这项研究的目的是利用不同的信息系统地可视化药物之间的关系。虽然已有研究使用结构化数据(如化学描述符)进行可视化研究，但尚未从全面的角度使用关于疗效、不良事件和其他现象的非结构化数据进行研究。因此，我们使用拓扑数据分析映射器和空间感知方法来获取和可视化基于定量数据和定性数据的综合主成分得分的数据。最后，给出了一个由药物类别特征聚类组成的网络。研究结果表明，该簇中的多相化合物可能表明药物重新定位的可能性。我们提出的方法是获取药学新知识的有效手段。
Recent years have witnessed the accumulation of vast amounts of data and information. It is difficult to capture the characteristics of these data spatially or visualize them robustly and stably with respect to data updates and increases using conventional methods. The purpose of this study is to systematically visualize the relationships among drugs using diverse information. While studies have conducted visualization research using structured data, such as chemical descriptors, research has not yet been performed from comprehensive viewpoints using unstructured data on efficacy, adverse events, and other phenomena. Therefore, we use a topological data analysis mapper and a spatial perception method to obtain and visualize data based on the integrated principal component score of quantitative and qualitative data. Consequently, a network composed of characteristic clusters according to drug class was shown. Findings show that heterogeneous compounds in the cluster may indicate the potential for drug repositioning. Our proposed method is an effective means of obtaining new knowledge of pharmaceuticals.