dc.contributor.advisor |
Rangwala, Huzefa |
|
dc.contributor.author |
Rasheed, Zeehasham
|
|
dc.creator |
Rasheed, Zeehasham |
|
dc.date.accessioned |
2013-08-09T15:40:05Z |
|
dc.date.available |
2013-08-09T15:40:05Z |
|
dc.date.issued |
2013 |
en_US |
dc.identifier.uri |
https://hdl.handle.net/1920/8285 |
|
dc.description.abstract |
Advances in biotechnology have dramatically changed the manner of characterizing large populations of microbial communities that are ubiquitous across several environments. The process of "metagenomics" involves sequencing of the genetic material of organisms co-existing within ecosystems ranging from ocean, soil and human body. Researchers are trying to determine the collective microbial community or population of microbes that co-exist across different environmental and clinical samples. Several researchers and clinicians have embarked on studying the pathogenic role played by the microbiome (i.e., the collection of microbial organisms within the human body) with respect to human health and disease conditions. |
|
dc.format.extent |
143 pages |
en_US |
dc.language.iso |
en |
en_US |
dc.rights |
Copyright 2013 Zeehasham Rasheed |
en_US |
dc.subject |
Computer science |
en_US |
dc.subject |
Bioinformatics |
en_US |
dc.subject |
Bioinformatics |
en_US |
dc.subject |
Clustering and Classification |
en_US |
dc.subject |
Data Mining |
en_US |
dc.subject |
Hashing |
en_US |
dc.subject |
Machine Learning |
en_US |
dc.subject |
Metagenomics |
en_US |
dc.title |
Data Mining Framework For Metagenome Analysis |
en_US |
dc.type |
Dissertation |
en |
thesis.degree.level |
Doctoral |
en |
thesis.degree.discipline |
Computer Science |
en |
thesis.degree.grantor |
George Mason University |
en |