MILVA: An interactive tool for the exploration of multidimensional microarray data

Davide D'Alimonte; David Lowe; Ian T Nabney; Vassilis Mersinias; Colin P Smith

doi:10.1093/bioinformatics/bti676

MILVA: An interactive tool for the exploration of multidimensional microarray data

Davide D'Alimonte, David Lowe^*, Ian T Nabney, Vassilis Mersinias, Colin P Smith

^*Corresponding author for this work

Computer Science Research Group

Research output: Contribution to journal › Article › peer-review

Abstract

Clustering techniques such as k-means and hierarchical clustering are commonly used to analyze DNA microarray derived gene expression data. However, the interactions between processes underlying the cell activity suggest that the complexity of the microarray data structure may not be fully represented with discrete clustering methods.

Original language	English
Pages (from-to)	4192-4193
Number of pages	2
Journal	Bioinformatics
Volume	21
Issue number	22
Early online date	13 Sept 2005
DOIs	https://doi.org/10.1093/bioinformatics/bti676
Publication status	Published - 2005

Keywords

cluster analysis
computational biology
computer graphics
statistical data interpretation
gene expression regulation
Internet
oligonucleotide array sequence analysis
automated pattern recognition
probability
programming languages
sensitivity and specificity
sequence alignment
DNA sequence analysis
software
user-computer interface

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title = "MILVA: An interactive tool for the exploration of multidimensional microarray data",

abstract = "Clustering techniques such as k-means and hierarchical clustering are commonly used to analyze DNA microarray derived gene expression data. However, the interactions between processes underlying the cell activity suggest that the complexity of the microarray data structure may not be fully represented with discrete clustering methods.",

keywords = "cluster analysis, computational biology, computer graphics, statistical data interpretation, gene expression regulation, Internet, oligonucleotide array sequence analysis, automated pattern recognition, probability, programming languages, sensitivity and specificity, sequence alignment, DNA sequence analysis, software, user-computer interface",

author = "Davide D'Alimonte and David Lowe and Nabney, {Ian T} and Vassilis Mersinias and Smith, {Colin P}",

year = "2005",

doi = "10.1093/bioinformatics/bti676",

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AU - D'Alimonte, Davide

AU - Lowe, David

AU - Nabney, Ian T

AU - Mersinias, Vassilis

AU - Smith, Colin P

PY - 2005

Y1 - 2005

N2 - Clustering techniques such as k-means and hierarchical clustering are commonly used to analyze DNA microarray derived gene expression data. However, the interactions between processes underlying the cell activity suggest that the complexity of the microarray data structure may not be fully represented with discrete clustering methods.

AB - Clustering techniques such as k-means and hierarchical clustering are commonly used to analyze DNA microarray derived gene expression data. However, the interactions between processes underlying the cell activity suggest that the complexity of the microarray data structure may not be fully represented with discrete clustering methods.

KW - cluster analysis

KW - computational biology

KW - computer graphics

KW - statistical data interpretation

KW - gene expression regulation

KW - Internet

KW - oligonucleotide array sequence analysis

KW - automated pattern recognition

KW - probability

KW - programming languages

KW - sensitivity and specificity

KW - sequence alignment

KW - DNA sequence analysis

KW - software

KW - user-computer interface

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UR - http://bioinformatics.oxfordjournals.org/content/21/22/4192

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DO - 10.1093/bioinformatics/bti676

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SP - 4192

EP - 4193

JO - Bioinformatics

JF - Bioinformatics

IS - 22

ER -

MILVA: An interactive tool for the exploration of multidimensional microarray data

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Keywords

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