Research - Our Team

Paula Petrone

Paula Petrone

Associate Research Professor

Paula Petrone holds a PhD degree in Biophysics from Stanford University. She completed her undergraduate degree in Physics at Instituto Balseiro in Argentina. Her field of expertise is the integration and mining of heterogeneous information at the intersection of chemistry, biology and medicine, using data analytics and machine learning.

As a Presidential Postdoc Fellow at Novartis NIBR, and later as a Senior Data Scientist at Roche, she has developed several machine learning models applied to drug discovery. Her postdoctoral work in Neuroscience at Barcelona Brain Research Centre combines imaging and machine learning to predict early Alzheimer's disease before the appearance of cognitive decline.

In the last years, she has served as a data science advisor for pharmaceutical companies and biotech startups and directed data science teams focusing on the analysis of real-world evidence based on patient data to understand human disease and make drug development more efficient.

Presently, she is Associated Research Professor at ISGlobal leading the Biomedical Data Science team.

Lines of research

Biomedical Data Science and Artificial Intelligence applied to:
  • Early diagnosis, risk assessment and management of chronic conditions
  • Mental health and neurodegeneration
  • Health informatics, real-world evidence from patients and wearables
  • Medical imaging

Main publications

  • PM. Petrone, A Casamitjana, et al. Prediction of amyloid pathology in cognitively unimpaired individuals using voxelwise analysis of longitudinal structural brain MRI. Alzheimer's Research & Therapy. 2019.
  • AC. Cabrera, E. Melo, D. Roth, A. Topp, F. Delobel, C. Stucki, C. Chen, P. Jakob, B. Banfai, T. Dunkley, O. Schilling, S. Huber, R. Iacone, PM. Petrone. HtrA1 activation is driven by an allosteric mechanism of inter-monomer communication. Sci. Rep. 2017.
  • AC. Cabrera, D. Lucena, I. Barasoain, F. Diaz, B. Fasching, PM. Petrone*. Aggregated compound biological signatures facilitate phenotypic virtual screening and target elucidation. ACS Chem. Biol., 2016.
  • PM. Petrone, B. Simms, F. Nigsch, E. Lounkine, P. Kutchukian, A. Cornett, Z. Deng, JW. Davies, JL. Jenkins, M. Glick. Rethinking molecular similarity: Comparing compounds on the basis of biological activity. ACS Chem. Biol., 2012.
  • PM. Petrone, V. Pande. Side-chain recognition and gating in the ribosome exit tunnel. Proc. Natl. Acad.Sci. USA, 2008.