Bioinformatics Toolbox 3.2

Product Description

• Bioinformatics Toolbox Key Features
• Microarray Data Analysis and Visualization
• Mass Spectrometry Data Analysis
• Graph Theory, Statistical Learning, and Gene Ontology
• Sequence Analysis
• Importing Data and Deploying Applications

Graph Theory and Visualization

Bioinformatics Toolbox includes functionality to apply basic graph theory to sparse matrices. Functions, objects, and methods are provided for creating, viewing, and manipulating graphs, such as interaction maps, hierarchy plots, and pathways. Examples include determining and viewing shortest paths in graphs, testing for cycles in directed graphs, and finding isomorphism between two graphs.

Statistical Learning and Visualization

Bioinformatics Toolbox provides functions that build on the classification and statistical learning tools in Statistics Toolbox. 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.

Gene Ontology

Bioinformatics Toolbox provides functionality for accessing the Gene Ontology Database from within MATLAB, parsing Gene Ontology annotated files, and obtaining subsets of the ontology, such as ancestors, descendents, or relatives.