Ole Lund 

Professor, DTU Health Technology, Bioinformatic

E-mail: olund@dtu.dk

Ole Lund is Professor of Bioinformatics at DTU Health Tech, specializing in health bioinformatics and personalized medicine. His research focuses on the application of bioinformatics, machine learning, and other computational methods to address complex challenges in microbiology, genomics, and antimicrobial resistance.

With more than 30 years of experience in bioinformatics and machine learning, he has contributed to the development of several internationally recognized methods for predicting protein structure, glycosylation, and epitopes. He led the bioinformatics development of a multilocus sequence typing method for bacteria, which later evolved into ResFinder, one of the most widely used tools for antimicrobial resistance analysis. The scientific publications describing these methods have received thousands of citations, and technologies developed by his research group are now used by clinical microbiology and public health surveillance laboratories in Denmark and internationally.

Professor Lund’s group has also developed methods for training statistical models and machine learning algorithms on whole genomes to predict an organism’s phenotype based on its genomic sequence. In addition, the group has applied these approaches to the analysis of human genomes.

 

DTU Health Tech, Bioinformatics

DTU Health Tech advances health and quality of life through innovative technologies in prevention, diagnostics, and treatment. The department combines expertise from fields like physics, biology, and computer science to create sustainable healthcare.

The Bioinformatics section combines biology and informatics to address questions relevant to human health and fundamental biological research. Key areas include personalized medicine, where treatments are tailored using genomic, transcriptomic, and metabolomic data, and machine learning applications for studying receptor-ligand interactions. Collaborations with research, industrial, and medical partners ensure the work remains practical and impactful. The section develops widely used bioinformatics tools and prediction servers, supporting the broader scientific community.