AWS for Industries

Category: Life Sciences

VPC in Cloud

Using miniwdl, GWFCore, and SageMaker Studio as a cloud IDE for genomics workflows

To keep pace with the growing scale of genomics datasets, bioinformatics scientists rely on shared analysis workflows written with portable standards such as the Workflow Description Language (WDL). The race to track SARS-CoV-2 variants notably illustrates rapid deployment of such workflows at scale on many platforms, including AWS. This post presents a new solution for […]

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Driving Life Sciences Manufacturing “Industry 4.0” using Image Analytics

Introduction This blog summarizes the use of image-based computing patterns in life sciences manufacturing to accelerate the cloud journey and realize predictive plant capabilities as foundational steps toward an “Industry 4.0” vision. AWS computer vision services automate discrete process steps using image analytics. We explore core business challenges and opportunities, target use-cases and AWS architecture […]

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Deploying multi-physics simulations for biopharma process development on AWS

This blog was co-authored by Fabrice Schlegel, Senior Manager of Data Sciences at Amgen; Joao Alberto de Faria, Senior Associate Software Engineer at Amgen; Ammar Latif, Senior SA at AWS; and Pierre-Yves Aquilanti, Principal HPC Specialist SA at AWS. Amgen relies on computational modeling to gain insight into their biopharma processes. Modeling improves product designs through […]

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Introducing AWS for Health – Accelerating innovation from benchtop to bedside

Healthcare and life science organizations are moving towards digital transformation to decrease the cost of care, improve collaboration, make data-driven clinical and operational decisions, and enable faster development of new therapeutics and treatment paths. Identifying the right cloud technology to reach these goals can be challenging, and many organizations lack the internal resourcing and expertise […]

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Running GATK workflows on AWS: a user-friendly solution

This post was co-authored by Michael DeRan, Scientific Consultant at Diamond Age Data Science; Chris Friedline, Scientific Consultant at Diamond Age Data Science; Netsanet Gebremedhin, Scientific Consultant at Diamond Age Data Science (Computational Biologist at Parexel at time of publication); Jenna Lang, Specialist Solutions Architect at AWS; and Lee Pang, Principal Bioinformatics Architect at AWS.  […]

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Executive Conversations: The era of genomics in the cloud with Peter Goodhand, CEO, Global Alliance for Genomics & Health

Executive Conversations: The era of genomics in the cloud with Peter Goodhand, CEO, Global Alliance for Genomics & Health

Peter Goodhand, CEO of the Global Alliance for Genomics & Health (GA4GH), joins Lisa McFerrin, Worldwide Lead of Genomic Bioinformatics at AWS, to discuss how secure storage and responsible sharing of genomic data in the cloud can benefit human health. GA4GH is a nonprofit alliance dedicated to creating frameworks and standards that facilitate data sharing […]

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Exploring the UniProt protein knowledgebase with AWS Open Data and Amazon Neptune

Example graph of protein data The Universal Protein Resource (UniProt) is a widely used resource of protein data that is now available through the Registry of Open Data on AWS. Its centerpiece is the UniProt Knowledgebase (UniProtKB), a central hub for the collection of functional information on proteins, with accurate, consistent and rich annotation. UniProtKB […]

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Training Machine Learning Models on Multimodal Health Data with Amazon SageMaker

Training Machine Learning Models on Multimodal Health Data with Amazon SageMaker

This post was co-authored by Olivia Choudhury, PhD, Partner Solutions Architect; Michael Hsieh, Sr. AI/ML Specialist Solutions Architect; and Andy Schuetz, PhD, Sr. Startup Solutions Architect at AWS. This is the second blog post in a two-part series on Multimodal Machine Learning (Multimodal ML). In part one, we deployed pipelines for processing RNA sequence data, clinical […]

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Example visualization of a CT scan, with lung tumor mask overlaid in yellow

Building Scalable Machine Learning Pipelines for Multimodal Health Data on AWS

This post was co-authored by Olivia Choudhury, PhD, Partner Solutions Architect; Michael Hsieh, Senior AI/ML Specialist Solutions Architect; and Andy Schuetz, PhD, Sr. Partner Solutions Architect. Healthcare and life sciences organizations use machine learning (ML) to enable precision medicine, anticipate patient preferences, detect disease, improve care quality, and understand inequities. Rapid growth in health information […]

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