Human-microbiome-relations extraction method with context-dependent clustering and semantic analysis

Shiori Hikichi, Shiori Sasaki, Yasushi Kiyoki

研究成果: Conference contribution

抄録

Human-microbiome-relations extraction is important for analyzing the effects on human gut microbiome from the difference of human attributes such as country, sex, age and so on. Human gut microbiome, a set of bacteria, provides various pathological and biological impacts on a hosting human body system. This paper presents a new analytical method for data resources that are difficult to understand such as human gut microbiome, by extracting the unknown relations with other adjunct metadata (e.g. human attributes data) with context-dependent clustering and semantic analysis. This method realizes the significant bacterial components acquisition for categorizing human attributes. The most important feature of our method is to analyze the unknown relations of human-microbiome with or without a correlation between a human attribute and bacteria that is found by related studies in bacteriology. With this method, an analyst is able to grasp the overview of bacteria data clustered by several clustering algorithms (k-means clustering / hierarchical clustering) using bacteria data selected by human attributes as a set of context. In addition, even without an association between a human attribute and bacteria as heuristic knowledge, an analyst is able to extract human-microbiome-relations focusing on a number of bacteria selected from all bacteria combinations by one-way analysis of variance (ANOVA) and our original criteria called the 'degree of separation' of clustering. This paper also presents an experimental study about human-microbiome-relations extraction and the experimental results that show the feasibility and effectiveness of this method.

元の言語English
ホスト出版物のタイトルInformation Modelling and Knowledge Bases XXVIII
出版者IOS Press
ページ258-273
ページ数16
292
ISBN(電子版)9781614997191
DOI
出版物ステータスPublished - 2017

出版物シリーズ

名前Frontiers in Artificial Intelligence and Applications
292
ISSN(印刷物)09226389

Fingerprint

Bacteria
Semantics
Bacteriology
Analysis of variance (ANOVA)
Metadata
Clustering algorithms

ASJC Scopus subject areas

  • Artificial Intelligence

これを引用

Hikichi, S., Sasaki, S., & Kiyoki, Y. (2017). Human-microbiome-relations extraction method with context-dependent clustering and semantic analysis. : Information Modelling and Knowledge Bases XXVIII (巻 292, pp. 258-273). (Frontiers in Artificial Intelligence and Applications; 巻数 292). IOS Press. https://doi.org/10.3233/978-1-61499-720-7-258

Human-microbiome-relations extraction method with context-dependent clustering and semantic analysis. / Hikichi, Shiori; Sasaki, Shiori; Kiyoki, Yasushi.

Information Modelling and Knowledge Bases XXVIII. 巻 292 IOS Press, 2017. p. 258-273 (Frontiers in Artificial Intelligence and Applications; 巻 292).

研究成果: Conference contribution

Hikichi, S, Sasaki, S & Kiyoki, Y 2017, Human-microbiome-relations extraction method with context-dependent clustering and semantic analysis. : Information Modelling and Knowledge Bases XXVIII. 巻. 292, Frontiers in Artificial Intelligence and Applications, 巻. 292, IOS Press, pp. 258-273. https://doi.org/10.3233/978-1-61499-720-7-258
Hikichi S, Sasaki S, Kiyoki Y. Human-microbiome-relations extraction method with context-dependent clustering and semantic analysis. : Information Modelling and Knowledge Bases XXVIII. 巻 292. IOS Press. 2017. p. 258-273. (Frontiers in Artificial Intelligence and Applications). https://doi.org/10.3233/978-1-61499-720-7-258
Hikichi, Shiori ; Sasaki, Shiori ; Kiyoki, Yasushi. / Human-microbiome-relations extraction method with context-dependent clustering and semantic analysis. Information Modelling and Knowledge Bases XXVIII. 巻 292 IOS Press, 2017. pp. 258-273 (Frontiers in Artificial Intelligence and Applications).
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