The rise of large foundational models across various domains, including chemical structures, cell types, and proteins, highlights a common challenge: the need for extensive and diverse data. However, these models often spend significant computational resources trying to capture patterns in …

Biology is ripe for machine learning, but there remains one central limitation: biological data is often expensive to generate and time-consuming to collect. This means that most companies trying to leverage machine learning are working with small datasets. One of the most powerful paradigms in this …

From advanced chatbots, to AI-generated images, we are seeing applications of generative AI grow by the day. But what actually is generative AI? This article aims to break down the core components of generative AI models and demonstrate applications in the biotech field. Key concepts: latent …