Next-Generation Genomic Sequencing

Making sense of extremely large volumes of genomic sequencing data 

AI/ML-powered genomic sequencing techniques offer new computational approaches to streamlining key analytical problems in genomic medicine. Making sense of extremely large volumes of genomic sequencing data and integrating it with other molecular and clinical data is a major challenge.

High-performance computing for genetic sequencing

The aim of genomic sequencing is to determine the order of the four chemical building blocks - called "bases" - that make up the DNA molecule. The sequence tells scientists the kind of genetic information that is carried in a particular DNA segment. When compared to large stretches of DNA from multiple bases, the sequence information can yield an enormous amount of information about the role of inheritance in susceptibility to disease and in response to environmental influences. In addition, the ability to sequence the genome more rapidly and cost-effectively creates vast potential for diagnostics and therapies. 

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Extreme cooling performance from the data center to the Edge

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Ultra-efficient and highly scalable Edge Computing

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Zero-touch computing for extreme edge locations

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