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Stallergenes is a Norwell, MA-based company in the Healthcare, Pharmaceuticals, and Biotech sector.
Centerfirst was founded in early 2004 to help healthcare leaders maximize the value of their call centers.
As a pioneer in the development of automated validation software solutions, ValGenesis leads the way with a unique paperless validation process that tracks the validation status of any GxP system in real time. With years of development and refinement in place, ValGenesis is the first to offer users real-time paperless validation via electronic execution thereby minimizing ineffectiveness and maximizing efficiency. ValGenesis automates and manages the validation life cycle and provides real time validation status of any system corporate wide.
Vaxxinity is a U.S.-based global biotechnology company pioneering a new class of medicines to democratize health.
insitro is a data-driven drug discovery and development company that leverages machine learning and high-throughput biology to transform the way medicines are created to help patients. At insitro, we are rethinking the entire drug discovery process, from the perspective of machine learning, human genetics, and high-throughput, quantitative biology. Over the past five decades, we have seen the development of new medicines becoming increasingly more difficult and expensive, leaving many patients with significant unmet need. We`re embarking on a new approach to drug development – one that leverages machine learning and unique in vitro strategies for modeling disease state and designing new therapeutic interventions. We aim to eliminate key bottlenecks in traditional drug discovery, so we can help more people sooner and at a much lower cost to the patient and the healthcare industry. We believe that by harnessing the power of technology to interrogate and measure human biology, we can have a major impact on many diseases. We invest heavily in cutting edge bioengineering technologies to enable the construction of large-scale, high-quality data sets that are designed specifically to drive machine learning methods. Our first application is to use human genetics, functional genomics, and machine learning to build a new generation of in vitro human cell-derived disease models whose response to perturbation is designed to be predictive of human clinical outcomes.