Advances in artificial intelligence (AI) and deep learning have fundamentally revolutionized many aspects of our lives, and research and technology is no exception. Application of AI models to a variety of problems in life sciences is a rapidly growing field. As pioneers in this field, we have a long history of developing neural network models to answer key questions in genomics. Modern novel neural network architectures as well as access to the computational resources required to deploy them has been a boon for computational genomics. From interpretable models for studying long-range combinatorial injections in DNA/RNA to large language models and foundation models in chemistry and genomics, we have enjoyed a resurgence in the promise of AI/ML in biology.

Over the past decade, cancer progression has emerged as a complex evolutionary process with many dynamic forces at play at every step. The resulting widespread reprogramming of the gene expression landscape in cancer cells is a hallmark of cancer development. While the focus of cancer biologists has been on the key signaling pathways and regulatory programs that are hijacked by cancer cells, my group has been interested in the possibility of emergent regulatory modules that are engineered by cancer cells and fall outside of existing regulatory networks. This question led us to the discovery of orphan non-coding RNAs (oncRNAs), a class of small non-coding RNAs that are generally not expressed in normal tissue. We have demonstrated that cancer cells can adopt oncRNAs to carry out new regulatory functions that promote metastatic progression.