Book chapter
Data Analysis for Gut Microbiota and Health
Advances in experimental medicine and biology, pp 79-87
01 Jan 2017
PMID: 29058217
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
In recent years, data mining and analysis of high-throughput sequencing of microbiomes and metagenomic data enable researchers to discover biological knowledge by characterizing the composition and variation of species across environmental samples and to accumulate a huge amount of data, making it feasible to infer the complex principle of species interactions. The interactions of microbes in a microbial community play an important role in microbial ecological system. Data mining provides diverse approachs to identify the correlations between disease and microbes and how microbial species coexist and interact in a host-associated or natural environment. This is not only important to advance basic microbiology science and other related fields but also important to understand the impacts of microbial communities on human health and diseases.
Metrics
Details
- Title
- Data Analysis for Gut Microbiota and Health
- Creators
- Xingpeng Jiang - Central China Normal UniversityXiaohua Hu - Central China Normal University
- Contributors
- B Shen (Editor)
- Publication Details
- Advances in experimental medicine and biology, pp 79-87
- Series
- Advances in Experimental Medicine and Biology
- Publisher
- Springer Nature; CHAM
- Number of pages
- 9
- Resource Type
- Book chapter
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000755064500005
- Scopus ID
- 2-s2.0-85032360676
- Other Identifier
- 991019167347704721
UN Sustainable Development Goals (SDGs)
This publication has contributed to the advancement of the following goals:
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Mathematical & Computational Biology
- Medical Informatics
- Medicine, Research & Experimental