Life sciences have a long history of driving advancements in various disciplines, including mathematics, philosophy, and logic. In recent years, life sciences have also become a significant application area for Artificial Intelligence (AI) technologies, including for neuro-symbolic AI methods. The life sciences knowledge infrastructure, characterized by its widespread use of ontologies, complex annotation models, large size, and community standards, presents unique challenges and opportunities for neuro-symbolic AI. We outline how neuro-symbolic methods have been applied and developed to address these challenges. We describe semantic similarity measures, knowledge graph embeddings, ontology embeddings, and knowledge-enhanced learning in the context of formalized life science knowledge. While there has been significant progress, we also outline multiple remaining challenges that provide opportunities for future research.