Iker Nunez Carpintero

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PhD student working in the Machine Learning for Biomedical Reaserch Unit and the Computational Biology Group (Life Sciences department). 
Main Research fields:
Complex and multilayer biomedical network analysis for Rare Disease and Cancer multi-omics
Deep learning for Cancer Research
Previous work:

 - MSc Student at Instituto de Biología y Genética Molecular (IBGM). Mucosal Immunology Lab, led by Eduardo Arranz and José Antonio Garrote Adrados. (September 2017 to July 2018)

 - BSc Student at Spanish National Centre for Biotechnology (CNB). Computational Systems Biology Group, led by Florencio Pazos (February to July 2017)


Degree University Year
BSc in Biology Universidad de Alcalá 2013-2017
MSc in Biomedical Research Instituto de Biología y Genética Molecular (IBGM) - Universidad de Valladolid (UVa) - Consejo Superior de Investigaciones Científicas (CSIC)


PhD in Biomedicine / Bioinformatics Barcelona Supercomputing Center (BSC-CNS) - Universitat de Barcelona (UB) 2018-Current


Núňez-Carpintero, I., O’Connor, E., Rigau, M., Bosio, M., Azuma, Y., Topf, A., Thompson, R., Hoen, P.A.C. ’t, Chamova, T., Tournev, I., Guergueltcheva, V., Laurie, S., Beltran, S., Capella-Gutierrez, S., Cirillo, D., Lochmūller, H. and Valencia, A., (2023). 'Rare disease research workflow using multilayer networks elucidates the molecular determinants of severity in Congenital Myasthenic Syndromes.' bioRxiv. https://doi.org/10.1101/2023.01.19.524736
Armaos, A., Serra, F., Núñez-Carpintero, I., Seo, J.-H., Baca, S.C., Gustincich, S., Valencia, A., Freedman, M.L., Cirillo, D., Giambartolomei, C., Tartaglia, G.G., (2022) bioRxiv . 'Reconstructing protein interactions at enhancer-promoter regions in prostate cancer'. https://doi.org/10.1101/2022.10.20.512998

Núñez-Carpintero, I., Petrizzelli, M., Zinovyev, A., Cirillo, D. and Valencia, A. (2021). 'The multilayer community structure of medulloblastoma'. iScience 24. https://doi.org/10.1016/j.isci.2021.102365

Cirillo, D., Núñez‐Carpintero, I. and Valencia, A. (2021) ‘Artificial intelligence in cancer research: learning at different levels of data granularity’, Molecular Oncology, 15(4), pp. 817–829. doi: https://doi.org/10.1002/1878-0261.12920.