Computational Biology and AI

TECNUN's Computational Biology group is a team multidisciplinary with extensive experience in development of optimisation algorithms, statistical analysis, as well as machine learning and deep learning methods, applied to different biological problems. We focus mainly on human health by integrating data from high performing (genomics, transcriptomics, proteomics, metabolomics, etc.) and instructions from data biological (genomic, pharmacological, metabolomic, etc.). Below is a snapshot of the projects we are currently working on.

• Metabolic reprogramming in cancer for the identification of new therapeutic targets and biomarkers of response.

• Precision oncology. Integration of large-scale gene silencing and pharmacological experiments.

• Alternative splicing in cancer. Alterations, causes and effects.

• Predictive models of drug toxicity based on structural features of the molecules.

• Influence of the gut microbiota in the context of health and nutrition.

• Compression schemes adapted to different omics data. Active members of development of the MPEG-G standard for the representation of genomic information.

• DNA sequencing analysis. Methods for germline enhancement and somatic variant calling.

• Gene regulatory network (GRN) inference for bulk and single-cell RNAseq data.

• Characterisation of HERVs (human endogenous retroviruses) in cancer and brain samples.

We actively collaborate with different research centres and companies. Among others, we list CIMA, Clínica Universidad de Navarra, CIC BioGUNE, Biodonostia, FISABIO, Onkologikoa, University of Granada, Celgene, Biobide, Polytechnic University of Catalonia, University of Illinois at Urbana-Champaign, Stanford University and University of the Republic (Uruguay).