Classification methods for the analysis of LH-PCR data associated with inflammatory bowel disease patients

The human gut is one of the most densely populated microbial communities in the world. The interaction of microbes with human host cells is responsible for several disease conditions and of criticality to human health. It is imperative to understand the relationships between these microbial communit...

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Vydané v:International journal of bioinformatics research and applications Ročník 11; číslo 2; s. 111
Hlavní autori: Wisittipanit, Nuttachat, Rangwala, Huzefa, Sikaroodi, Masoumeh, Keshavarzian, Ali, Mutlu, Ece A, Gillevet, Patrick
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Switzerland 2015
Predmet:
Algorithms
Bacteria - genetics
Bacteria - isolation & purification
Humans
Inflammatory Bowel Diseases - microbiology
Intestinal Mucosa - microbiology
Microbiota - genetics
Pattern Recognition, Automated - methods
Polymerase Chain Reaction - methods
Reproducibility of Results
Sensitivity and Specificity
Support Vector Machine
length heterogeneity PCR
IBD
kNN
K–nearest neighbour
SVM
classification
machine learning
inflammatory bowel disease
human gut
microbial abundance profile
support vector machine
human microbiome
LH–PCR fingerprinting
bioinformatics
polymerase chain reaction
ISSN:1744-5485
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Shrnutí:The human gut is one of the most densely populated microbial communities in the world. The interaction of microbes with human host cells is responsible for several disease conditions and of criticality to human health. It is imperative to understand the relationships between these microbial communities within the human gut and their roles in disease. In this study we analyse the microbial communities within the human gut and their role in Inflammatory Bowel Disease (IBD). The bacterial communities were interrogated using Length Heterogeneity PCR (LH-PCR) fingerprinting of mucosal and luminal associated microbial communities for a class of healthy and diseases patients.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1744-5485
DOI:10.1504/IJBRA.2015.068087