Bioinformatics Toolbox 3.0 MS

Category Proteomics>Mass Spectrometry Analysis/Tools

Abstract Bioinformatics Toolbox 3.0 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. The toolbox provides access to genomic and proteomic data formats, analysis techniques, and specialized visualizations for genomic and proteomic sequence and microarray analysis. Most functions are implemented in the open MATLAB (see Note 1) language, enabling you to customize the algorithms or develop your own. Key features include:

Mass Spectrometry Data Analysis:

A set of functions is provided for mass spectrometry data analysis. The tools are designed for preprocessing, classification, and marker identification from surface-enhanced laser desorption/ionization (SELDI), matrix-assisted laser desorption/ionization (MALDI), liquid chromatography (LC)/mass spectrometry (MS), and gas chromatography (GC)/MS data. Preprocessing functions include baseline correction, smoothing, calibration, and resampling. You can align raw spectra data using the mass/charge (M/Z) axis and perform retention time alignment on LC/MS and GC/MS data. A graphical user interface (GUI) lets you view multiple spectra simultaneously.

Tutorials provide step-by-step examples of how to smooth, align, and normalize spectra and then use classification and statistical learning tools to create classifiers and identify potential biomarkers.

Statistical Learning and Visualization:

Bioinformatics Toolbox provides functions that build on the classification and statistical learning tools in the Statistics Toolbox (see Note 2). These include support vector machine (SVM) and K- nearest neighbor classifiers; functions for setting up cross-validation experiments and for measuring the performance of different classification methods; and tools for selecting discriminating features. Graph viewing and manipulation tools let you display interaction maps, hierarchy plots, and pathways.

Additional Key Features include:

Note 1: MATLAB is a high-level language and interactive environment that enables you to perform computationally intensive tasks faster than with traditional programming languages such as C, C++, and FORTRAN.

Note 2: Statistics Toolbox extends MATLAB to support a wide range of common statistical tasks. The toolbox contains two (2) categories of tools - 1) Building-block statistical functions for use in MATLAB programming; 2) Graphical user interfaces (GUIs) for interactive data analysis.

System Requirements

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Manufacturer

Manufacturer Web Site The MathWorks

Price Contact manufacturer.

G6G Abstract Number 20054

G6G Manufacturer Number 102630