Featured Article
Industrialize Safety and Efficacy Biomarker Discovery
Today's biopharmaceutical companies face a critical need to move beyond first generation bioinformatics tools. New scientific information technology solutions can be a discovery engine for precision medicine programs, enabling data-driven hypothesis generation and patient stratification to accelerate the discovery of safety and efficacy biomarkers from molecular, clinical, image, and RWE data. By simplifying the day-to-day challenge of working with multiple lines of evidence, a scientific database management system can support clinical guidance for programs such as precision gemcitabine treatment.
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Changing the Equation in Drug Development: The Important Role of Bio-Informatics in "Drug Rescue"
Recent estimates cite the risk-adjusted cost of bringing a new drug to approval north of $2.5 billion. This is a staggering amount, particularly when less than 10 percent of newly innovated therapeutics survive the clinical development process. Indeed, drug development is fraught with high costs and even higher risk. Biopharma companies need a way to change the cost-risk equation and ensure the balance is tipped toward returns.
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Featured Video
Accelerating Cancer Informatics At Foundation Medicine
Much can be learned from proper analysis of large sets of genomic data. In this video, Dr. Eric Neumann, VP of Knowledge Informatics, Foundation Medicine, describes a few examples of scalable analytics applied to cancer genomics, and how SciDB-R enables finding subtle multi-gene dependencies that may suggest underlying mechanisms. Combining statistical analysis with other other knowledge discovery tools can help accelerate the transformation of large data sets into biological insights.
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Featured White Paper
Turning Big Data Into Knowledge
Biological data is flooding the pharmaceutical and medical sectors. Advances in sequencing technology are making it possible to create the world of precision medicine—except for one problem: first generation bioinformatics software tools do not readily allow data to be searchable or sharable, thus precluding easy analysis. By separating data storage from computing too much time is spent moving data instead of analyzing data. What's needed is a new paradigm for scientific data management and analysis.
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