Literature DB >> 12651728

MatArray: a Matlab toolbox for microarray data.

David Venet1.   

Abstract

The microarray technology allows the high-throughput quantification of the mRNA level of thousands of genes under dozens of conditions, generating a wealth of data which must be analyzed using some form of computational means. A popular framework for such analysis is Matlab, a powerful computing language for which many functions have been written. However, although complex topics like neural networks or principal component analysis are freely available in Matlab, functions to perform more basic tasks like data normalization or hierarchical clustering in an efficient manner are not. The MatArray toolbox aims at filling this gap by offering efficient implementations of the most needed functions for microarray analysis. The functions in the toolbox are command-line only, since it is geared toward seasoned Matlab users.

Mesh:

Year:  2003        PMID: 12651728     DOI: 10.1093/bioinformatics/btg046

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  7 in total

1.  Gene ordering in partitive clustering using microarray expressions.

Authors:  Shubhra Sankar Ray; Sanghamitra Bandyopadhyay; Sankar K Pal
Journal:  J Biosci       Date:  2007-08       Impact factor: 1.826

2.  Gene expression in human thyrocytes and autonomous adenomas reveals suppression of negative feedbacks in tumorigenesis.

Authors:  Wilma C G van Staveren; David Weiss Solís; Laurent Delys; David Venet; Matteo Cappello; Guy Andry; Jacques E Dumont; Frédérick Libert; Vincent Detours; Carine Maenhaut
Journal:  Proc Natl Acad Sci U S A       Date:  2005-12-28       Impact factor: 11.205

3.  MBEToolbox: a MATLAB toolbox for sequence data analysis in molecular biology and evolution.

Authors:  James J Cai; David K Smith; Xuhua Xia; Kwok-Yung Yuen
Journal:  BMC Bioinformatics       Date:  2005-03-22       Impact factor: 3.169

4.  Optimized LOWESS normalization parameter selection for DNA microarray data.

Authors:  John A Berger; Sampsa Hautaniemi; Anna-Kaarina Järvinen; Henrik Edgren; Sanjit K Mitra; Jaakko Astola
Journal:  BMC Bioinformatics       Date:  2004-12-09       Impact factor: 3.169

5.  Absence of a specific radiation signature in post-Chernobyl thyroid cancers.

Authors:  V Detours; S Wattel; D Venet; N Hutsebaut; T Bogdanova; M D Tronko; J E Dumont; B Franc; G Thomas; C Maenhaut
Journal:  Br J Cancer       Date:  2005-04-25       Impact factor: 7.640

6.  RNA-seq and microarray complement each other in transcriptome profiling.

Authors:  Sunitha Kogenaru; Yan Qing; Yinping Guo; Nian Wang
Journal:  BMC Genomics       Date:  2012-11-15       Impact factor: 3.969

7.  The discrimination of interaural level difference sensitivity functions: development of a taxonomic data template for modelling.

Authors:  Balemir Uragun; Ramesh Rajan
Journal:  BMC Neurosci       Date:  2013-10-07       Impact factor: 3.288

  7 in total

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