Søren Brunak

Current positions

2025-:

Head of Section for Health Data Science and AI, Dept. of Public Health, University of Copenhagen

2007–:

Professor of Disease Systems Biology, Dept. of Public Health & Novo Nordisk Foundation Center for Protein Research, University of Copenhagen

Previous positions

2015–2024:

Senior Medical Informatics Officer, Rigshospitalet, Copenhagen, Denmark

1999–2021:

Professor of Bioinformatics, Department of Systems Biology, Technical University of Denmark

1997–1998:

Associate professor, Department of Biotechnology, Technical University of Denmark, Kongens Lyngby, Denmark

1993–2015:

Center director, Center for Biological Sequence Analysis, Technical University of Denmark, Kongens Lyngby, Denmark (1993–1997 at the Department of Chemistry)

1990–1993:

Senior researcher, Department of Physics, Technical University of Denmark, Kongens Lyngby, Denmark

1987–1990:

Research associate, Department of Structural Properties of Materials, Technical University of Denmark, Kongens Lyngby, Denmark

1982–1984:

Administrative head, Andersen & Simonsen I/S, Ulfborg, Denmark

Education

2002:

Dr.phil. (honoris causa), Natural Science Faculty, Stockholm University, Sweden

1991:

PhD in Computational Biology, Department of Structural Properties of Materials, Technical University of Denmark

1987:

MSc in Physics, Niels Bohr Institute, University of Copenhagen, Denmark

Research awards

2019:

The Nordic Prize from the Eric K. Fernström Foundation

2019:

The Kirsten and Freddy Johansens Medical Preclinical prize

2017:

Honorary Award of the University Hospital of Zealand 2017, awarded to a pioneer who has broken conventions and created new paths through one’s work  

2016:

Senior Scientist Award, International Society for Computational Biology (ISCB)

2016–:

Member of the Royal Swedish Academy of Sciences

2013:     

Julius Thomsen’s Gold Medal awarded by Technical University of Denmark

2012:

The Order of Dannebrog, Danish Royal Order of Chivalry

2011–:   

Chair of the Interim ELIXIR Board

2011–:   

ISCB Fellow

2009–:   

Member of EMBO

2009:

Grundfos Prize 2009 for crucial and ground-breaking research within systems biology and bioinformatics

2007–2010:

Chair of the Awards Committee of the International Society for Computational Biology

2006:

Villum Kann Rasmussen Prize for Research within the Natural and Technical Sciences

2004–:   

Member: Danish Royal Society of Science and Letters

2002:     

Dir. Ib Henriksens Prize for Outstanding Science Achievement

2001–:

Member of the Danish Academy of Technical Sciences

2001–2004: 

Board of directors, International Society for Computational Biology

1998: 

Bjerrum-Brønsted-Lang Award from the Royal Danish Academy of Sciences and Letters.

Title of lecture: Protein structure prediction by artificial neural networks

Søren Brunak is a leading pioneer in the biomedical sciences through invention and introduction of new computational strategies for analysis of large biological data sets for use in biology, medicine and biotechnology. His main achievements can be divided into two categories: 1) new, advanced bioinformatics and systems biology techniques, and 2) discovery of biological mechanisms, revealed using these methods in a wide range of biological systems. The impact of Brunak’s research is in particular a consequence of his ability to combine scientific disciplines in novel ways. His multi-disciplinary approaches, where concepts from different areas have been combined, have led to advances in the understanding of the function of biological systems, and thereby fundamentally improved the possibilities for control of disease via novel intervention strategies, and enhancement of health in general. Søren Brunak has published more than 500 peer reviewed papers and co-authored four books, Google Scholar ~155,000 citations, H-index of 128. Included in the Clarivate listing of “Highly Cited Researchers”, which citation-wise belong to the top 1% in their discipline in a number of years, also in 2024. Similarly, for the Stanford listing of the 2% most impactful researchers.

Early bioinformatics focus

Søren Brunak has been working within bioinformatics and computational biology since the mid-1980ies and thus started his research in these areas before they became hot topics. In the early data-poor period Søren Brunak pioneered the introduction of new computational strategies for analysis of biological data of relevance in molecular biology, medicine and biotechnology – in particular machine learning techniques. Building on good publications from this period (including one in Nature 1990, 343:123) Brunak in 1992 obtained major funding from the newly established Danish National Research Foundation to create a large bioinformatics effort, and became the founding Director of the Center for Biological Sequence Analysis (CBS), which was formed at the Technical University of Denmark (DTU) in 1993 as a multi-disciplinary research group of molecular biologists, biochemists, medical doctors, physicists, and computer scientists. After more than 20 years, this effort developed into being one of the large and most renowned bioinformatics centers within academia in Europe, eventually with twelve independent research groups and staff exceeding 160.

From biological sequence analysis to integrative disease systems biology

Throughout his career, Søren Brunak has been focusing on work which went beyond the state of the art within computational biology. He has often made the “first prediction method” of a given type, including inventing new conservative ways of curating and preparing the underlying data, which in turn has led to the creation of high-quality prediction tools. The artificial neural network approach has been one of his key areas (FEBS Lett. 1988, 241:223) – a methodology that later has been taken up by thousands of other researchers within bioinformatics worldwide. An innovative aspect in the use of the machine learning techniques was that Søren Brunak combined different predictors trained on the same data and used them to define several complementary input-output relationships. This often raised the predictive performance by an order of magnitude (JMB 1991:220:49). The idea of combining complementary prediction tools was later implemented in numerous prediction schemes developed by other bioinformaticians around the world. In his own sequence analysis work, the signal peptide prediction method SignalP is a prime example, now at more than 25,000 citations (Prot. Eng. 1997 10:1-6).

After the year 2000, Søren Brunak’s work developed into becoming much more oriented towards data integration. Machine learning techniques were first used to integrate predicted or experimentally established functional genome and proteome annotation with the aim of predicting protein function; subsequently many other systems biology-oriented schemes for data integration across experimental data types were constructed. In the data integration area, he and his colleagues also made many quite original papers, often being first in terms of what they integrated and the methodology used to do it (JMB 2002 319:1257; Mol. Cell 2006 22:285; Nature Biotech. 2007 25:309; PNAS 2008 105:20870). An example from integrative systems biology concerns the modeling of the eukaryotic cell cycle and its dynamical representation as protein-protein interaction networks (Science 2005 307:724; Nature 2006 443:594; Science Signal. 2010 3(104):ra3). Here the aim was to discover new basic regulatory principles for protein complex activation, and to study dynamic changes in global levels of modification of cell cycle related proteins, in particular different types of kinase-specific phosphorylation, as well as protein degradation. Later Søren Brunak has among other topics been concentrating on different aspects of human variation, e.g. the Danish reference genome and antique DNA sequencing (Nature 2010 463:757; Nat Commun. 2015 6:5969; Nature 2015 522:167; Nat Commun. 2015 6:5969; Nature 2017 548:87) as well as disease systems biology, metagenomics and metametabolomics, which also led to significant findings published well (Nature 2010, 464:59; Mol. Syst. Biol. 2010, 6:381; Nature Biotech. 2014 8:822; Nature 2015 528:262; Nature 2016 535:376; Nat Microbiol. 2016 1:16152).

His more recent incorporation of healthcare sector data in data integration frameworks benefits from the existence and accessibility of electronic patient records in the Danish setting (PLoS Biol. 2011 7:e1002141; Nature Rev. Genetics 2012 13:395; Drug Saf. 2014 7:237; Nature Rev. Genetics 2016 17:615). He has for example developed a disease trajectory concept for modeling of multi-morbidity and comorbidity patients that has attracted significant attention (Nature Commun. 2014, 5:4022) and is used to analyse disease progression patterns, for example in diabetes and other chronic diseases (Elife 2019 8:e44941; J Am Heart Assoc. 2020 9:e015069; Alzheimers Dement. 2020 16:908). Gender differences in disease progression patterns using millions of trajectories is another example (Nature Commun. 2019). These approaches have also been used recently in the big data ICU patient domain (Lancet Dig. Health 2019 1:E78; Lancet Dig. Health 2020 2:PE179). Further recent impactful papers on disease progression include (Nature Biotech. 2023 41:399 and Nature Med. 2023 29:1113). Disease trajectory heritability has also been investigated (Nat Commun 2024 15:7457). His current research on combining molecular level systems biology and health care sector data such as electronic patient records and biobank questionnaires continues the big data integration strategy ((Nature Biotech. 2023 41:399). The aim is to stratify patients not only from their genotype, but also phenotypically based on the clinical descriptions in the medical records. He has been developing a general approach for gathering phenotypic descriptions of patients from medical records in a systematic and non-cohort dependent manner, for example used to study mixed etilogies where patients are member of several subgroups in a single disease area at the same time (Cell Rep Med. 2022 3:100477). By extracting phenotype information from the free text in such records he has demonstrated that the information contained in the records can be used for producing fine-grained patient stratification and disease co-occurrence statistics. Disease correlations identified can subsequently be mapped to systems biology frameworks with the general aim of finding genes which play a role in more than one disease. Machine learning and AI continues to be an important tool (Lancet Oncol. 2024 25:e694-e703). 

Major cross-institutional efforts in Denmark

Søren Brunak became one of the founding research directors at the Novo Nordisk Foundation Center for Protein Research at the University of Copenhagen (2007-). His program for Disease Systems Biology combines molecular level systems biology and the analysis of phenotypic data from the healthcare sector. A general aim is to understand disease mechanisms at the level of protein network biology using a wide range of molecular data integrated with clinical data such as electronic patient records, registry information, and biobank questionnaires. Patient-specific adverse drug reaction profiles and the discrimination between treatment related disease correlations and other comorbidities are investigated in the context of human proteome variation.  One of the key study domains revolves around diabetes progression, in particular insulin resistance, associated cardiovascular disease, diabetes complications, and general systematic patterns in time-ordered diabetes complications. Different routes towards type 1 and type 2 diabetes (diseases which manifest prior to diabetes diagnoses) and related patient stratification models are major themes. In 2011 Søren Brunak was also one of the founders of the Novo Nordisk Foundation Center for Biosustainability at DTU and UCPH, where he led the Section for Metagenomic Systems Biology (2011-2013). The focus there was on understanding metagenomic data from a wide range of ecological niches and to exploit them in biotechnological contexts.

Services to the national and international scientific communities

Søren Brunak has been serving on a large number of scientific advisory boards and international funding bodies, including 2004-2007: Chair of the Bioinformatics Advisory Committee at the European Bioinformatics Institute, 2007-2010: Member of the "Molecules, Genes and Cells Funding Committee" at the Wellcome Trust, 2007-2011: Membership and chairing of the ERC panel for "Genetics, genomics, bioinformatics and systems biology", and 2008-2011: Member of the Scientific Council at the Institut Pasteur, Paris. From 2011-2013 he was Chair of the Interim Board for the European ESFRI infrastructure for bioinformatics, ELXIR, where more than 20 countries jointly worked on establishing a new distributed infrastructure covering all of Europe with significant interfaces to North and South America and Asia. He has been involved in organising numerous scientific meetings in Denmark, Europe and beyond, including chairing “Intelligent Systems for Molecular Biology”, the world’s largest bioinformatics conference gathering 1,500 participants in Copenhagen in 2001. Similarly, Søren Brunak has been active in establish supercomputer infrastructure for use by the Danish Life Science community. In 2015 he was one of the two coordinators of a 40-page document that describes the perspectives around personalized treatment and precision medicine in Denmark. The document played an important role in the process that eventually let to a new law in 2018 on the establishment of a National Genome Center that is constructing a single database for whole genome sequencing data for patients in Denmark.