In the last twenty years, data science has become a crucial tool in biomedical research. At the CNIC Bioinformatics Unit we blend the latest developments in Computational Biology and Statistical modeling with deep molecular phenotyping of large human cohorts and animal models to improve our understanding of cardiovascular diseases and also of aging-related disorders.

To achieve this broad goal we have three main areas of research:

  1. Omics data analysis: Ad-hoc analysis and integration of omics data (transcriptomics, methylomics, genomics for germline and somatic variants) in bulk samples or at the single-cell level in animal models and in human samples, to understand the underlying molecular mechanisms of health and disease. For that, we prefer probabilistic models inferred using Bayesian statistics to take into account the noisy and heterogeneous nature of various types of omics data.
  2. Cardiovascular data science: Exploring the use of machine learning methods for outcome prediction through the integration of molecular and deep phenotyping information in large human prospective and retrospective cohorts. In this area we also work on the development of a data-warehousing solutions expanding the already implemented CNICtranSMART.
  3. In silico prediction of protein structure for drug design and simulation of changes in protein structure produced by DNA variants.

All statistical methods and tools developed by the Bioinformatics Unit can be found at:

Areas of research