Abstract
Extraction of gut-microbes information is important for analyzing the effects on human gut microbiome from the difference of human attributes such as nationality, gender, age and so on. It is pointed out that human gut microbiome, a set of bacteria, has various pathological and biological impacts on a hosting human body system. However, analyzing and estimating such kinds of impact from biological data resources are difficult even for data analysts with biological background. This paper presents MicroSIA, a new analytical method for human gut microbiome's effect by extracting the unknown relations with other adjunct metadata such as human attributes with Semantic Inverse Analysis. The most important feature of our method is the inverse processes (Semantic Inverse Analysis, computing the selection of axes in inversed direction to clustering) to discover potentially existing bacteria-combinations for classifying nationalities in human attribute data. MicroSIA extracts unique bacteria-combination selected from all bacteria-combinations by our original criteria such as the purity of a data cluster and the range of target human attributes. This paper also presents experimental studies on gut-microbes information acquisition to show the feasibility and the effectiveness of our method.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - IEEE 11th International Conference on Semantic Computing, ICSC 2017 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 9-16 |
| Number of pages | 8 |
| ISBN (Electronic) | 9781509048960 |
| DOIs | |
| Publication status | Published - 2017 Mar 29 |
| Event | 11th IEEE International Conference on Semantic Computing, ICSC 2017 - San Diego, United States Duration: 2017 Jan 30 → 2017 Feb 1 |
Other
| Other | 11th IEEE International Conference on Semantic Computing, ICSC 2017 |
|---|---|
| Country | United States |
| City | San Diego |
| Period | 17/1/30 → 17/2/1 |
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Keywords
- Bacterial components acquisition
- Data mining
- Inverse problem
- Personalised medicine
- Semantic analysis
ASJC Scopus subject areas
- Computer Science Applications
- Information Systems
- Computer Networks and Communications
Cite this
MicroSIA : A Gut-Microbes Information-Extraction Method with Semantic Inverse Analysis for Discovering Unique Bacteria-Combinations in Nationality. / Hikichi, Shiori; Kiyoki, Yasushi.
Proceedings - IEEE 11th International Conference on Semantic Computing, ICSC 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 9-16 7889497.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - MicroSIA
T2 - A Gut-Microbes Information-Extraction Method with Semantic Inverse Analysis for Discovering Unique Bacteria-Combinations in Nationality
AU - Hikichi, Shiori
AU - Kiyoki, Yasushi
PY - 2017/3/29
Y1 - 2017/3/29
N2 - Extraction of gut-microbes information is important for analyzing the effects on human gut microbiome from the difference of human attributes such as nationality, gender, age and so on. It is pointed out that human gut microbiome, a set of bacteria, has various pathological and biological impacts on a hosting human body system. However, analyzing and estimating such kinds of impact from biological data resources are difficult even for data analysts with biological background. This paper presents MicroSIA, a new analytical method for human gut microbiome's effect by extracting the unknown relations with other adjunct metadata such as human attributes with Semantic Inverse Analysis. The most important feature of our method is the inverse processes (Semantic Inverse Analysis, computing the selection of axes in inversed direction to clustering) to discover potentially existing bacteria-combinations for classifying nationalities in human attribute data. MicroSIA extracts unique bacteria-combination selected from all bacteria-combinations by our original criteria such as the purity of a data cluster and the range of target human attributes. This paper also presents experimental studies on gut-microbes information acquisition to show the feasibility and the effectiveness of our method.
AB - Extraction of gut-microbes information is important for analyzing the effects on human gut microbiome from the difference of human attributes such as nationality, gender, age and so on. It is pointed out that human gut microbiome, a set of bacteria, has various pathological and biological impacts on a hosting human body system. However, analyzing and estimating such kinds of impact from biological data resources are difficult even for data analysts with biological background. This paper presents MicroSIA, a new analytical method for human gut microbiome's effect by extracting the unknown relations with other adjunct metadata such as human attributes with Semantic Inverse Analysis. The most important feature of our method is the inverse processes (Semantic Inverse Analysis, computing the selection of axes in inversed direction to clustering) to discover potentially existing bacteria-combinations for classifying nationalities in human attribute data. MicroSIA extracts unique bacteria-combination selected from all bacteria-combinations by our original criteria such as the purity of a data cluster and the range of target human attributes. This paper also presents experimental studies on gut-microbes information acquisition to show the feasibility and the effectiveness of our method.
KW - Bacterial components acquisition
KW - Data mining
KW - Inverse problem
KW - Personalised medicine
KW - Semantic analysis
UR - http://www.scopus.com/inward/record.url?scp=85018254268&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85018254268&partnerID=8YFLogxK
U2 - 10.1109/ICSC.2017.65
DO - 10.1109/ICSC.2017.65
M3 - Conference contribution
AN - SCOPUS:85018254268
SP - 9
EP - 16
BT - Proceedings - IEEE 11th International Conference on Semantic Computing, ICSC 2017
PB - Institute of Electrical and Electronics Engineers Inc.
ER -