Title :
Using pairwise difference features to measure temporal changes in the microbial ecology
Author :
M. Yazdani;L. Smarr
Author_Institution :
California Institute for Telecommunications and Information Technology, University of California San Diego USA
Abstract :
The exponential affordability of DNA sequencing technologies has enabled not only the accessible and rapid sequencing of the human genome, but the opportunity to sequence vast numbers of multi-cellular organism and the tiniest microbes. Studying the microbes in the human body has led us to the view that the human body is not a single organism but rather a symbiotic ecology of microbes and human cells. The microbiome is referred to the collection of microorganisms in our body (consisting mostly of bacteria, archaea, and some eukarya) and outnumber our human cells ten to one. Numerous studies suggest that the microbiome may play a critical role in a number of autoimmune diseases, with most research on inflammatory bowel disease. The microbiome ecology can be thought of as a composition of the relative abundance of a large number of organisms that reside in our bodies. Numerous studies suggest that disruptions in the relative abundance of a healthy microbial ecology leads to a disease state. The challenge remains, however, in understanding how and what changes in the composition of the ecology leads to a disease state.
Keywords :
"Ecology","Diseases","Big data","Sequential analysis","Microorganisms","Data analysis"
Conference_Titel :
Big Data (Big Data), 2015 IEEE International Conference on
DOI :
10.1109/BigData.2015.7364124