GENALICE today announced it has completed its prototype 2 product performance testing. The load time was as expected very short and the footprint very small for each recorded defect.

GENALICE develops a new groundbreaking data processing and analysis platform. It is designed to perform faster, cheaper and much more reliable analyses. GENALICE is capable of processing more than 1 million DNA samples from over 150,000 patients in a single run. By incorporating this platform cancer patients could get their treatment faster, more effective and with minimum adverse effects. This new IT technology can make an enormous contribution to oncology.

“We are very pleased with the results of the tests,” said the CTO of Genalice. “Our DNA load performance lines up with Oracle’s Exadata database performance, which can be seen as a standard in this area.” The very small footprint allows GENALICE to store DNA data from large numbers of patients with minimum storage requirements. “The company is perfectly on track with the product development of its information and analysis platform for oncology”. “We expect to introduce the first version of our product in the second quarter of 2012. With our groundbreaking technology GENALICE wants to contribute to the transformation of cancer from a deadly disease into a chronic and possibly even into a preventable disease. Our primary goal is to save lives and improve the quality of life of cancer patients. “

Mark Clark, Director e-health & Life Sciences EMEA from Hitachi Data Systems mentions the following: “Key people make a key difference. GENALICE is a company with people who have an in depth knowledge of high volume, high performance data processing technology coupled a passion to really make a life changing difference to people living with cancer. They have our full support!”

GENALICE combines the robustness of a world-class database with the performance and flexibility of custom-made software. This technology is generally applicable, not only in the field of bioinformatics, but in all areas where a small data footprint, high speed data interpretation, transformation and correlation of data from different sources is required.