Remove tag drug-discovery
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Meta’s Next-Generation Image Segmentation: Paving the Way for Advanced Applications

Mlearning.ai

Output using Meta’s SAM for tagging boxes Quality Management in manufacturing Image Segmentation can be used for quality management in manufacturing by identifying defective items. Image segmentation can accelerate drug discovery by analyzing images of cells and tissues and automating the screening process.

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Understanding neural networks: A beginner’s guide

Data Science Dojo

Here are some key real-world applications: Facial recognition : Neural networks are used in facial recognition technologies, which are prevalent in security systems, smartphone unlocking, and social media for tagging photos.

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What Is a Transformer Model?

Hacker News

They’re helping researchers understand the chains of genes in DNA and amino acids in proteins in ways that can speed drug design. Transformers use positional encoders to tag data elements coming in and out of the network. Attention units follow these tags, calculating a kind of algebraic map of how each element relates to the others.

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

AWS Machine Learning Blog

Proteins also play a key role in drug development, as potential targets but also as therapeutics. As shown in the following table, many of the top-selling drugs in 2022 were either proteins (especially antibodies) or other molecules like mRNA translated into proteins in the body. Top companies and drugs by sales in 2022.

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Use RAG for drug discovery with Knowledge Bases for Amazon Bedrock

AWS Machine Learning Blog

In this post, we demonstrate how to build a RAG workflow using Knowledge Bases for Amazon Bedrock for a drug discovery use case. Use the following pieces of information to provide a concise answer to the question enclosed in tags. This data is information rich but can be vastly heterogenous.

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The most valuable AI use cases for business

IBM Journey to AI blog

Clarify computer vision AI-powered computer vision enables image segmentation , which has a wide variety of use cases, including aiding diagnosis in medical imaging, automating locomotion for robotics and self-driving cars, identifying objects of interest in satellite images and photo tagging in social media.

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Artificial Intelligence trends in 2023

How to Learn Machine Learning

Generative Models: Generative models, which can generate new images, sounds, and text, will become more sophisticated and more widely used in areas such as content creation, data augmentation, and drug discovery. Tags: Artificial Intelligence trends, AI Trends, Artificial Intelligence, AI projects.