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Data is everywhere. The ability to extract and harness data is the driving force behind the exciting new field of data science. ChromaCode, a next-generation health care company, is redefining diagnostics through data science, starting with multiplex molecular diagnostics. Data science uses statistics, data analysis, machine learning and their related mathematical methods in order to understand phenomena with data.

This revolutionary field is drastically changing how industries approach problem-solving and value creation. Multiplex molecular diagnostics is considered the gold standard for a wide variety of clinical and research applications, but its high cost and need for specialized instrumentation is limiting further adoption.

ChromaCode is overcoming this limitation through the use of data science to provide a next generation multiplexing solution that leverages existing instrumentation and widely used low-cost chemistries. ChromaCode has developed a novel multiplexing technology called high definition polymerase chain reaction, or HDPCR for short. HDPCR enhances multiplexing levels of the most common real-time and digital PCR instruments for test applications of 5 to 50 targets without requiring any instrument modifications.

While traditional multiplexing relies on differentiation of targets by color, HDPCR differentiates multiple targets by signal intensity. This amplitude modulation method generates unique PCR curve signatures for each target or combination of targets encoded into a single-color channel. To ensure reproducible results on a single instrument and across multiple instruments, HDPCR utilizes proprietary signal-processing algorithms to account for instrument-specific norms.

Through data science, ChromaCode is empowering the more than 80,000 real-time and digital PCR systems globally to perform multiplex testing at a very low cost. Using HDPCR, ChromaCode is seeking to broaden access to advanced diagnostics, reduce healthcare costs and rapidly address unmet healthcare testing needs.

ChromaCode: redefining diagnostics through data science.