TY - GEN
T1 - Spatial Perception for Structured and Unstructured Data In topological Data Analysis
AU - Kitanishi, Yoshitake
AU - Ishioka, Fumio
AU - Iizuka, Masaya
AU - Kurihara, Koji
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Quantitative and qualitative data
KW - Spatial perception
KW - Structured and unstructured data
KW - Topological data analysis
KW - Topological data analysis mapper
UR - http://www.scopus.com/inward/record.url?scp=85102728315&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85102728315&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-60104-1_12
DO - 10.1007/978-3-030-60104-1_12
M3 - Conference contribution
AN - SCOPUS:85102728315
SN - 9783030601034
T3 - Studies in Classification, Data Analysis, and Knowledge Organization
SP - 103
EP - 111
BT - Data Analysis and Rationality in a Complex World
A2 - Chadjipadelis, Theodore
A2 - Lausen, Berthold
A2 - Markos, Angelos
A2 - Lee, Tae Rim
A2 - Montanari, Angela
A2 - Nugent, Rebecca
PB - Springer Science and Business Media Deutschland GmbH
T2 - 16th Conference of the International Federation of Classification Societies, IFCS 2019
Y2 - 26 August 2019 through 29 August 2019
ER -