Methods and models for biological Big Data

Using network theory, statistical mechanics and mathematical models (deterministic and stochastic), novel algorithms and approaches are developed and applied for the analysis of high-throughput omics data (gene expression, proteomics, metabolomics, epigenomics, next-generation sequencing) and imaging data (structural and functional NMR, TAC, PET).

Keywords: physical modeling, statistical analysis, systems biology, network theory, personalized medicine, bioinformatics, biomedical imaging, big data analytics, data management.

Collaborations

Computer Laboratory - Cambridge University, Cambridge UK

Centre for Genomic Regulation - CRG, Barcelona ES

Barabasi Lab - Northeastern University, Boston USA

Department of Molecular Biology - Brown University, Providence USA

German Cancer Research Center - DKFZ, Heidelberg DE

Scuola Internazionale Superiore di Studi Avanzati - SISSA, Trieste IT

Istituto di Tecnologie Biomediche - ITB CNR, Milano IT

Istituto Tumori della Romagna - IRST IRCCS, Meldola IT

Policlinico S. Orsola, Bologna IT

Istituti Ortopedici Rizzoli - IOR, Bologna IT

Istituto Nazionale di Fisica Nucleare - INFN, IT