How can we better understand the vast complexity of the human genome? Personalized medicine requires a deep understanding of the genetic code in order to specifically detect and treat diseases. Nevertheless, large parts of the human genome remain, to date, not understood. To address this challenge, we developed Genolator - a multimodal AI system designed to bridge genetic code and human understanding. By integrating genomic sequence representations, protein structure information, and natural language models, Genolator allows researchers to query coding sequences in natural language and explore potential biological processes, molecular functions, and cellular roles associated with a sequence. This ability to connect diverse biological layers through natural language queries creates a new interface for exploring genomic data, helping researchers and physicians uncover functional relationships and generate new insights into the genome.
Martin Danner is a Senior Data Scientist and Machine Learning Engineer at scieneers, where he designs and builds scalable AI systems and cloud-based data platforms for organizations across industries including healthcare, energy, media, construction, and the public sector. His work focuses on bringing machine learning into production, spanning data engineering, cloud architecture, MLOps, and the deployment of large-scale AI systems. Alongside his industry work, he is pursuing a PhD at the Centre for Human Genetics and Genomic Medicine at RWTH Aachen University Hospital. His research explores the use of machine learning, genomic language models, and large language models to better understand previously understudied regions of the human genome, sometimes referred to as the “dark genome,” with applications ranging from variant interpretation to protein structure prediction and microprotein research. Working at the intersection of AI engineering, cloud platforms, and biomedical research, Martin regularly speaks about building AI infrastructure, deploying machine learning systems in production, and applying AI in genomics and healthcare.
