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MathWorks - Bioinformatics Toolbox
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The Bioinformatics Toolbox offers computational molecular biologists and other research scientists an open and extensible environment in which to explore ideas, prototype new algorithms, and build applications in drug research, genetic engineering, and other genomics and proteomics projects.
http://www.mathworks.com/products/bioinfo/
Submitted Apr 30, 2004
Updated Jun 24, 2004
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PhylLab
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PHYLLAB is a MATLAB toolbox for sequence manipulation and phylogenetic analysis. PHYLLAB takes as input a set of aligned nucleotide or amino-acid sequences, and performs phylogeny inference. It uses a Markov chain Monte Carlo method, evaluating the posterior distribution over tree topologies and a variety of model parameters, including parameters of substitution-rate variation under a wavelet model. The graphical interface helps users to manage input data and to visualize the most likely trees; they can also view substitution-rate plots that show the maximum posterior density (confidence) intervals. PHYLLAB is written completely in the MATLAB language. Although it does not implement all the multiplicity of existing methods used in phylogenetics, interested users can extend it easily.
http://amdec-bioinfo.cu-genome.org/html/misc/Pavel/phyllab.html
Submitted Sep 15, 2004
by Pavel
Updated Sep 17, 2004
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Free Online MathWorks Seminar: MATLAB for Bioinformatics
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Bioinformatics researchers and developers worldwide rely on MATLAB to accelerate scientific discovery and reduce development time. Developers in bioinformatics value the open, component-based architecture of MATLAB, which lets them choose only the tools and extensions needed.
http://www.mathworks.com/mtd6944
Submitted Aug 10, 2004
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MatArray Toolbox
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A MATLAB toolbox with the most common functions used in microarray analysis, mainly normalization and clustering.
http://www.ulb.ac.be/medecine/iribhm/microarray/toolbox/
Submitted Apr 30, 2004
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BSTLab: A MATLAB Toolbox for Biochemical Systems Theory
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To facilitate wider application of Biochemical Systems Theory (BST), we have begun development of a software toolbox compatible with Matlab, a popular scientific and engineering computing package. The toolbox, BSTLab, implements functions common to BST-based studies and is designed to integrate easily into the widely used Matlab environment, automate many common tasks, and allow expansion and customization by the user...
http://www.iscb.org/ismb2003/posters/schwackeATmusc.edu_235.html
Submitted Jun 15, 2005
by MATLAB Central Admin
Updated Mar 19, 2008
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MathWorks Computational Biology Conference
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Pharmaceutical R&D is becoming increasingly dependent on efficient computational methods. Isolating drug targets, determining toxicity early, and testing targets and compounds in silico are crucial steps to getting compounds to market more quickly. This conference will feature technical presentations from MathWorks staff as well as case studies from distinguished customers to demonstrate, how you can accelerate your research and gain deeper scientific insight by using computational methods.
http://www.mathworks.com/cb07
Submitted Mar 12, 2007
by Martin Stoller
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SBML plugin
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BML diagram type can be used for model simulation using MATLAB plug-in. The plugin will generate MATLAB files and starts MATLAB engine for model simulation and results visualization
http://www.biouml.org/plugin_sbml.shtml?user_guide#simulation
Submitted Feb 23, 2005
by Iulian Nicolae Ruda
Updated Feb 24, 2005
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MatArray toolbox
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The MatArray toolbox is a set of Matlab tools designed for the analysis of microarray data.
http://www.ulb.ac.be/medecine/iribhm/microarray/toolbox/
Submitted Jun 08, 2005
by MATLAB Central Admin
Updated Jun 13, 2005
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Systems Biology Toolbox 2 for MATLAB
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The Systems Biology Toolbox 2 for MATLAB offers systems biologists a powerful, open, and user extensible environment, in which to build models of biological systems. Experiments can be performed on models, just like in real lab life but in silico. The representation of models, experiments, and measurement data is intuitive and easy to use. The toolbox features a wide variety of specialized analysis tools and MATLAB adds to that by a large number of inbuilt functions and a high level scripting language, allowing the user to quickly and efficiently add new functionality.
The SBPD extension package for the Systems Biology Toolbox 2 adds high-speed simulations, combination of models, experiments, and measurement data in so called projects. Functions are available that support the complete model building process (modeling, simulation, identifiability analysis, model reduction, parameter estimation (multiple experiment and multiple measurement fitting), validation, etc.). The projects are a powerful construct that allows to keep a perfect overview over your modeling task at any time. Graphical user interfaces support the workflow.
http://www.sbtoolbox2.org
Submitted Jan 21, 2008
by Henning Schmidt
Updated Jan 22, 2008
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PGEToolbox - Population Genetics and Evolution Toolbox
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PGEToolbox is a Matlab-based software package for analysis of polymorphism and divergence data for population genetics and evolution. It estimates several basic statistics of DNA sequence variation and carries out statistical tests of selective neutrality under the infinite alleles model, such as Tajima's D test, Fu & Li's tests and Fay & Wu's H test. The significance of tests is determined from the distribution of the statistics obtained by coalescent simulation. The toolbox performs McDonald-Kreitman test (and several extensions). PGEToolbox also contains functions for handling SNP (Single Nucleotide Polymorphism) genotype data. PGEToolbox is open-sourced, can be easily extended or tailored for specific tasks, and scaled up for large data sets.
http://bioinformatics.org
Submitted Mar 24, 2007
by James J. Cai
Updated Apr 30, 2008
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PGEToolbox - Population Genetics and Evolution Toolbox
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This is a toolbox for analysis of polymorphism and divergence data in population genetics and evolution. It computes basic statistics of DNA sequence variation and carries out neutrality tests, such as Tajima's D test, Fu & Li's tests and Fay & Wu's H test. The significance of tests is determined from the distribution of the statistics obtained by coalescent simulation. The toolbox performs McDonald-Kreitman test (and several extensions), and also contains functions for handling SNP genotype and haplotype data.
http://bioinformatics.org/pgetoolbox/
Submitted Jun 21, 2007
by James J. Cai
Updated Aug 02, 2007
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vector-ga: A Vectorized Implementation of a Genetic Algorithm in Matlab
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"VectorGA is a vectorized implementation of a genetic algorithm in the Matlab programming language.
Matlab is optimized for performing operations on arrays. Loops, especially nested loops, tend to run slowly in Matlab. It is possible to significantly improve the performance of Matlab programs by converting loops into array operations. This process is called vectorization. Matlab provides a rich set of functions and many expressive indexing schemes that make it possible to vectorize code. Such code not only runs faster, it is also shorter, and simpler to understand and change (provided that you know a little about Matlab of course).
Genetic Algorithms that are implemented in C/C++ or Java typically have multiple nested loops. Therefore direct ports of such implementations to Matlab will run very slowly. Many of the nested loops found in a typical GA implementation have been eliminated from VectorGA. The resulting code is short, fast and simple. It is indeed a delightful coincidence when the constructs of a programming language match a programming task so well that a program can be written this succintly.
VectorGA is proof that Matlab is a useful language for the rapid prototyping of Genetic Algorithms. This, in addition to Matlab�s extensive data visualization capabilities, make Matlab an extremely useful platform for the experimental analysis of GAs.
VectorGA has been created and tested under Matlab 7 (R14). Out of the box it evolves a population against the one-max fitness function. The royal-roads fitness function has also been included but is not currently being called."
http://code.google.com/p/vector-ga/
Submitted Dec 13, 2007
Updated Dec 14, 2007
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gfit: model-based analysis of experimental data
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With gfit one can create a model for virtually any type of system using minimal amount of computer code. It is particularly useful for studying various systems in Biophysics, Biochemistry and Cell Biology. The interface for gfit models specifies relationships between input and output variables in a rule-based fashion. It provides flexibility and allows to re-use same models for many related problems.
For more information and for most current version of software visit the project's website: http://gfit.sourceforge.net
gfit analyzes data using models. One sample model is included in the distribution. A brief modeling guide can be found on the website. More models will be also shared through the website. I can provide help with simple models or collaborate on more complex ones.
http://gfit.sourceforge.net
Submitted Mar 27, 2007
by Mikhail Levin
Updated Aug 02, 2007
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MATLAB for Biotechnology
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Researchers throughout the biotechnology, pharmaceutical, and medical industries use MATLAB� for technical tasks that span the realm of computational biology, diagnostic medical imaging, and biosignal processing. MathWorks� products are ideally suited for modeling, simulating, analyzing, and visualizing complex biological data.
http://www.mathworks.com/industries/biotech/
Submitted Apr 10, 2008
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HPlus - A Software Application for Haplotype Estimation
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HPlus is a user-friendly software application for estimating haplotype frequencies, inferring individual haplotypes, and most significantly assessing haplotypic associations with various types of phenotypes. HPlus handles bi-allelic genetic markers such as SNPs, and multi-allelic genetic markers such as microsatellites. HPlus can perform association analysis using 1) binary phenotypes such as disease status from case-control studies or cohort studies, 2) continuous outcome(s) such as adulthood heights or biomarkers from clinical follow-up studies, cohort studies, or intervention studies, and 3) survival outcomes from clinic trials, cohort studies or clinical follow-up studies. Finally, HPlus allows you to incorporate covariates such as demographic or clinical variables into haplotypic association analyses. These covariates can be treated as confounding variables to be adjusted for during the haplotypic association analysis. In addition you can assess gene-environment interactions between covariates and haplotypes. HPlus also allow users to perform stratified analysis by grouping variables chosen by users.
http://cougar.fhcrc.org/hplus/
Submitted Apr 11, 2008
Updated Apr 14, 2008
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