BIG DATA CASE STUDY UCLA announced a new institute to help medical and biology researchers make sense of Big Data. Millions upon millions of medical records and test results. Countless DNA sequences. Hard drives stuffed with images of all kinds – pictures of cells, scans of body parts. It’s all part of the deluge of […]
In your world – numbers and data can save lives. Minutes and seconds absolutely matter. Whether engaged in genome sequencing, drug design, product analysis or risk management, life sciences research teams need high-performance technical environments with the ability to process massive amounts of data and support increasingly sophisticated simulations and analyses.
This webinar is focus on understanding active risk management with high performance data and grid management.
For a long time, the industry’s biggest technical challenge was squeezing as many compute cycles as possible out of silicon chips so they could get on with solving the really important, and often gigantic problems in science and engineering faster than was ever thought possible. Now, by clustering computers to work together on problems, scientists are free to consider even larger and more complex real-world problems to compute, and data to analyze.
Scientific research in the life sciences is often akin to searching for needles in haystacks. Finding the one protein, chemical, or genome that behaves or responds in the way the scientist is looking for is the key to the discovery process. For decades, high performance computing (HPC) systems have accelerated this process, often by helping to identify and eliminate in feasible targets sooner.
“Datameer is all about providing a self-service, end-to-end experience for big data analytics on Hadoop. From data integration to analytics to visualization, we are wizard-led, point-and-click. Most recently we announced our Smart Analytics module, which allows business users to use data mining algorithms through a drag and drop UI. These new capabilities complement what data scientists are doing and enable business analysts to take advantage of advanced algorithms without involving IT.”
Leading researchers in data science and genomics are recommending strategies to help genomic scientists better manage, share, analyze and archive massive research and clinical data sets in an effort to ensure that the big data explosion results in better health outcomes and faster research discoveries.