Disease intelligence and molecular evolution

Just like military intelligence or business intelligence, disease intelligence is a field that integrates large amounts of heterogeneous data from various sources in order to find patterns and correlations. In the case of disease intelligence, the aim is to understand diseases and improve treatments, and data sources include both hosts (e.g., genotype and acquired mutations) and pathogens. In addition to DNA and RNA sequences, we integrate data from metabolomics, metagenomics, environmental and lifestyle factors that influence disease susceptibility, disease progression, and treatment outcomes.

All this would not make sense except in the light of evolution. Evolutionary considerations tell us which variation (genetic or environmental) actually make a difference for the organism in health and disease, and analysis of pathogen evolution can help us trace outbreaks and understand transmission.

Here are some instances of projects we work with:

  • Genome sequencing: Interpreting whole genome sequencing from children with cancer, for clinical decisions relating to predisposition. Utilising the Danish reference genome as a source of improved imputation for clinical use.
  • Diseases and treatment:
    - Understanding the genomics and environment behind childhood leukaemia and childhood asthma. Integration of genomics, environment with Bayesian methods, statistical modelling and machine learning.
    - Integrating genomics, treatment data and clinical variables to understand and predict treatment related effects in childhood leukaemia and testicular cancer.
    - Exploring the fitness landscape of evolving Hepatitis C virus, predicting drug resistance from NGS sequenced blood samples.
  • Diet and metabolic health: Understanding what determines a metabolic healthy response to diet: genomics, metagenomics or an individual’s clinical characteristics?
  • Protein subcellular location prediction using deep learning on protein sequences.
  • Mutation prioritisation through use of sequence data and post-translational modifications.

The DIME group collaborates with other groups at DTU Bioinformatics, with other DTU departments (Compute, Food, Vet, Biosustain, Chemical engineering), with clinical groups at Rigshospitalet and Gentofte Hospital and with industry (Symphogen, ALK, Kopenhagen Fur, Clinical Microbiomics).




Anders Gorm Pedersen
DTU Bioinformatics
+45 45 25 61 08


Ramneek Gupta
Associate Professor
DTU Bioinformatics
+45 45 25 24 22