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NAViGaTOR | I2D | BTSVQ

Tools

Software analysis and modeling tools

Our focus is on network analysis and modeling, integrated with cancer profiles that will enable us to identify diagnostic and prognostic biomarkers, understand disease initiation and progression, which will lead to improving cancer treatment. Our tools, such as NAViGaTOR, I2D and BTSVQ enable users to interpret integrated cancer profiles, and create relevant models dynamically.

NAViGaTOR-Network Analysis, Visualization, & Graphing TORonto

NAViGaTOR-Network Analysis, Visualization, & Graphing TORonto

NAViGaTOR is a software package for visualizing and analyzing protein-protein interaction networks. NAViGaTOR can query OPHID / I2D - online databases of interaction data - and display networks in 2D or 3D. To improve scalability and performance, NAViGaTOR combines Java with OpenGL to provide a 2D/3D visualization system on multiple hardware platforms. NAViGaTOR also provides analytical capabilities and supports standard import and export formats such as GO and the Proteomics Standards Initiative (PSI).

In protein-protein interaction networks, nodes represent proteins, and edges between nodes represent physical interactions between the proteins.These visualizations can enable insights into the proteins that play key roles in diseases such as cancer.
Go to NAViGaTOR home page

I2D-Interologous Interaction Database

I2D-Interologous Interaction Database

I2D is an on-line database of known and predicted mammalian and eukaryotic protein-protein interactions. It has been built by mapping high-throughput (HTP) data between species. Thus, until experimentally verified, these interactions should be considered "predictions". I2D remains one of the most comprehensive sources of known and predicted eukaryotic PPI.
Go to I2D home page

BTSVQ-Binary tree structured vector quantization

BTSVQ-Binary tree structured vector quantization

BTSVQ is a computational tool to analyze and visualize microarray gene expression data. This technique merges the results of SOM (genes space), and partitive k-means (specimen space). The algorithm uses vector quantization and self-organizing capabilities of SOMs in finding significant gene centers in gene space (high dimensionality and large number of clusters), and the effectiveness of k-means in experiment space (medium dimensionality and low number of clusters).
Go to BTSVQ home page