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Pre-training genomic language models using AWS HealthOmics and Amazon SageMaker

AWS Machine Learning

The ability to effectively analyze and interpret genomic data at scale is the key to precision medicine, agricultural optimization, and biotechnological breakthroughs, making genomic language models a possible new foundational technology in these industries. e-]*)"}, {"Name": "train_perplexity", "Regex": "Train Perplexity: ([0-9.e-]*)"},

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Efficiently fine-tune the ESM-2 protein language model with Amazon SageMaker

AWS Machine Learning

About the Author Brian Loyal is a Senior AI/ML Solutions Architect in the Global Healthcare and Life Sciences team at Amazon Web Services. He has more than 17 years’ experience in biotechnology and machine learning, and is passionate about helping customers solve genomic and proteomic challenges.

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Accelerate protein structure prediction with the ESMFold language model on Amazon SageMaker

AWS Machine Learning

In addition to basic research, algorithms like AlphaFold and ESMFold have many applications in medicine and biotechnology. About the Authors Brian Loyal is a Senior AI/ML Solutions Architect in the Global Healthcare and Life Sciences team at Amazon Web Services. A score of 1.0 indicates a perfect match. Scores above 0.7 Scores above 0.9