Case Study


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NeoProteomics is a large-scale study formed on the heels of The Human Genome Project. It provides research services and tools for human disease detection and diagnosis, specifically cancer. The company focuses on accelerating the process of drug and diagnostics development by providing innovative analysis tools to help its clients understand genomics and proteomics data at the systems level.

2012 - 2014

Agile Development

Software Architecture Design

Technology Used

Java - Custom Data Visualizations

Cloud Computing - Amazon Web Services


Client feedback

“Freeport Metrics was able to provide a reliable and professional team for our projects, and they quickly grasped the scientific concepts that are part of our core algorithms. They have proven to be valuable partner.”

John L.H. Schenkel

Jr. President & CEO of NeoProteomics


A need to move to cloud computing architecture.

The newest cloud computing technology could accelerate work on disease detection and diagnosis and help ensure prompt results. 

New data visualization toolset for the domain.

A new product's data visualization and exploration biology tool could help identify diagnostics in human diseases, especially cancer.

Product offer expansion.

NeoProteomics needed to partner with a tech-savvy company to successfully assess and develop new features and solutions to help them expand their product offering.


Platform optimization.

We optimized the computation of NeoProteomics platform to help seek better disease detection and diagnosis results.

Assistance with moving to cloud computing.

For the core analysis engine, we assisted the move to cloud computing and provided architectural guidance on managing distributed computation jobs efficiently.

Custom data visualization.

We created custom data visualizations for the desktop Java application merged into a single dashboard for better analysis practices and comparison results.


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Well-built disease markers discovery.

The data visualization enabled NeoProteomics to image the results of each of their biomarker experiments - tests that help to drive the discovery of significant disease markers. 

More effective algorithm analysis and results.

Scientists could effectively analyze and compare the results of their algorithms during their discovery process with the collection of data visualizations consolidated into a single dashboard.

New opportunities discovered by the use of the markers.

These markers helped identify novel pathways and new drug targets, stratify patient populations, and, more importantly, better predict the most effective therapeutic response.

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