The MathWorks’ new Bioinformatics Toolbox may signal the company’s interest in courting the bioinformatics market, but developers in the sector discovered the company’s Matlab programming language well before the company launched the product. A list of bioinformatics applications developed for Matlab follows:
CGH-Plotter, from Finland’s Tampere University of Technology: A Matlab toolbox with a graphical user interface for the analysis of comparative genomic hybridization (CGH) microarray data. Available at http://sigwww.cs.tut.fi/TICSP/CGH-Plotter/.
Quantum Clustering, from Israel’s Tel Aviv University: A clustering method for microarray analysis written in Matlab. Available at http://neuron.tau.ac.il/~horn/QC.htm.
Fuzzy C-Means for Clustering Microarray Data, from France’s Institut de Genetique et de Biologie Moleculaire et Cellulaire: A set of Matlab functions for clustering microarray data. Available at http://www-igbmc.u-strasbg.fr/fcm/.
A numerical algorithm for studying biomolecular transport processes, from the University of California, Santa Cruz: A numerical algorithm that uses a continuous Markov process to study biomolecular transport processes. Matlab functions available at http://www.amath.unc.edu/Faculty/telston/matlab_functions/.
Molecular bioactivity predictor, from Germany’s Max Planck Institute: Methods for predicting molecular bioactivity for drug design, including a feature-selection method for unbalanced data and a classifier that adapts to the distribution of the unlabeled test data. Matlab source code available at http://www.kyb.tuebingen.mpg.de/bs/people/weston/kdd/kdd.html.
MatArray, from Belgium’s Campus Hopital Erasme: A Matlab toolbox for data normalization, hierarchical clustering, and other microarray analysis functions. Available at http://www.ulb.ac.be/medecine/iribhm/microarray/toolbox/.
MArray, from the Norwegian Radium Hospital: a Matlab toolbox with a graphical user interface that allows users to analyze single or paired microarray datasets. Available at http://matrise.uio.no/marray/marray.html.
BTSVQ (Binary tree-structured vector quantization), from the Ontario Cancer Institute: A technique that combines tree-structured vector quantization and partitive k-means clustering for analyzing microarray data, written in Matlab. Available at http://www.uhnres.utoronto.ca/ta3/BTSVQ.