Have you ever wondered how scientists are using cutting-edge technology to understand our DNA?

Join us as we explore the fascinating world of deep learning in genomics and discuss a new paper that examines how to make these powerful tools more interpretable.

Deep learning, a type of artificial intelligence, is revolutionizing many fields, including genomics. By analyzing vast amounts of genetic data, these complex algorithms can uncover hidden patterns and make predictions about our health, traits, and even our ancestry.

But there's a catch: deep learning models are often described as "black boxes" because it's difficult to understand how they reach their conclusions. This lack of transparency can make it challenging for scientists to trust and fully utilize these powerful tools. This is where interpretable deep learning comes in.

The paper we'll be discussing, "Designing Interpretable Deep Learning Applications in Genomics: a Quantitative Analysis", explores different approaches to open up the black box and shed light on the decision-making processes of these complex models