Different types of data require distinct generative AI models. Large language models excel at generating text, while diffusion models are designed for image generation. This article delves into how diffusion models work and their applications in biotechnology. Understanding Diffusion Models …

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 …