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<\/a>\u00a0<\/h3>\nThe technologies that are unlocking insights in research today<\/h3>\n
Big data is the key to medical research findings. It can take massive amounts of information to power big projects in genomics, bioinformatics, predictive biology, chemistry, pharmacology and so on. Fortunately, advanced technologies have emerged to enable researchers to put that data to work. These include:<\/p>\n
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- Data analytics<\/strong> \u2013 This involves gathering data; cleaning (or \u201cscrubbing\u201d) it to distill it down to a high-quality dataset without duplications or errors; creating models for analysis; data mining to identify patterns and anomalies; and interpreting the data and findings to uncover insights.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n
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- AI and machine learning (ML)<\/strong> \u2013 These technologies assist in data analytics because AI and ML can chew through enormous volumes of data very quickly and find those patterns and relationships faster and more efficiently than ever. With ML, a research platform can essentially teach itself and become smarter about what\u2019s relevant and what isn\u2019t as it receives more data. These platforms can help lead teams to insights they never would have noticed before.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n
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- High-performance computing (HPC)<\/strong> \u2013 Because research requires so much data and high-performance AI, ML and analytics systems, teams must use HPC systems designed to handle the intensive workloads research creates. In the past, the only HPC systems available were massive supercomputers typically owned by governments or educational institutions. Today, however, computing capabilities have advanced so much that, with the right software and applications, teams can achieve HPC levels of performance on commodity servers.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n
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Researchers typically use a combination of these technologies to get the necessary insights out of their data. With big data, a good analytics platform, AI, ML and HPC, today\u2019s researchers can create safer, more effective products, accelerate research phases, identify new molecules that have potential to treat diseases, improve product effectiveness, identify populations at risk for specific conditions, predict and respond to outbreaks more efficiently, predict post-surgery complications, streamline diagnosis, improve medical equipment\u2019s efficiency and much more.<\/p>\n
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<\/a>Read: SSD Advantages in Healthcare<\/div><\/div><\/div><\/div><\/div>\n<\/p>\n
Here\u2019s just one example of what can result when big data is combined with the latest technologies: The Department of Energy Office of Science<\/a> partnered with the National Institutes of Health in 1990 to sequence the entire 3 billion base pair human genome. The project took 10 years and cost almost $4 billion. Today, however, thanks to improved computing capabilities and other technology, a human genome can be sequenced in less than 24 hours for about half the price of the latest iPhone<\/a>.<\/p>\n
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Big data creates even bigger expectations in data storage<\/h3>\n
A recent article for Clinical Leader<\/a> states that in 2012, it was estimated that Phase 3 medical research studies collected close to 1 million data points. Today, however, healthcare data points are measured in the billions. The article then goes on to say: \u201cThis dramatic increase demands the adoption of new strategies to improve the collection, processing, and archiving of data supporting this new scale.\u201d<\/p>\n
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Data storage for medical research projects needs to be able to handle huge amounts of data without compromising performance. Systems that use AI, ML and data analytics must deliver low latency and high throughput. They must be able to support both read-intensive and write-intensive workloads. They require an infrastructure that can support quick scalability and not consume too much power.<\/p>\n
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Traditional data storage can no longer keep up with the storage needs of advanced medical and life sciences research. These systems are creating bottlenecks and slowing down research and clinical trials\u2014which means a longer wait for actual breakthroughs.<\/p>\n
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Part of the problem is that life sciences companies simply aren\u2019t undergoing digital transformation very quickly. A recent McKinsey report<\/a> found that, even in 2022, life sciences companies still lag behind digital maturity leaders in other industries by a factor or two to three times, \u201cwithout any clear signs of catching up.\u201d<\/p>\n
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These organizations need a new approach to data storage\u2014one that can support today\u2019s advanced demands. It should be able to break down data silos and make data sharing more efficient; easily scale as needed; and be able to support the high-performance demands of data analytics, HPC, AI and ML.<\/p>\n
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- High-performance computing (HPC)<\/strong> \u2013 Because research requires so much data and high-performance AI, ML and analytics systems, teams must use HPC systems designed to handle the intensive workloads research creates. In the past, the only HPC systems available were massive supercomputers typically owned by governments or educational institutions. Today, however, computing capabilities have advanced so much that, with the right software and applications, teams can achieve HPC levels of performance on commodity servers.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n
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- AI and machine learning (ML)<\/strong> \u2013 These technologies assist in data analytics because AI and ML can chew through enormous volumes of data very quickly and find those patterns and relationships faster and more efficiently than ever. With ML, a research platform can essentially teach itself and become smarter about what\u2019s relevant and what isn\u2019t as it receives more data. These platforms can help lead teams to insights they never would have noticed before.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n
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- Data analytics<\/strong> \u2013 This involves gathering data; cleaning (or \u201cscrubbing\u201d) it to distill it down to a high-quality dataset without duplications or errors; creating models for analysis; data mining to identify patterns and anomalies; and interpreting the data and findings to uncover insights.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n
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