Technology > Fractal Genomic Modeling
Support Vector Machine (SVM)

Recursive Feature Elimination (RFE)

Fractal Genomic Modeling (FGM)

Independant Validations

Health Discovery Corporation acquired the Fractal Genomics Modeling to find discriminate relationships within clinical datasets as well as within gene expression datasets created from micro-arrays of disease versus normal tissues.

Fractal Genomics Modeling (FGM) technology is designed to study complex networks, such as genes inside a living organism.  FGM uses a new approach toward modeling network behavior to rapidly generate diagrams and software simulations that facilitate prediction and analysis.  Two important concepts behind FGM technology are the notions of scale-free networks and self-similarity.

Using the FGM data analysis technique, HDC scientists have been able to access information in micro-array datasets and improve the mapping of genetic pathways involved in the diagnosis and prevention of certain diseases. HDC scientists find the FGM is effective for finding genes implicated in several cancers, HIV infection, lymphedema, Down's syndrome, and a host of other diseases. Additionally, the technique is effective for pharmacogenetic profiling of patients.

The Fractal Genomics Modeling Process
The FGM process starts out by creating a special mathematical surface (the FGM surface) where every point on the surface can be used to generate a network model with varying degrees of scale-free and fractal properties.

Using user-supplied data, models which best match the behavior of each node are selected and represented by a point on the FGM surface. These point-models are then linked, compared, and combined to generate diagrams which reflect the behavior of the entire network.

FGM derived diagrams expedite forecasting, analysis, and study of complex system behavior by clearly displaying all hubs, links, and flow in the network. These diagrams also serve as flow charts and schematics from which to build software simulations of the network. The FGM process can also be used to quickly search for networks and connections between processes and between objects that appear to be unrelated.
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