Iorio, Francesco

Drug repurposing: progress, challenges and recommendations

Pushpakom, S., Iorio, F., Eyers, P. A., Escott, K. J., Hopper, S., Wells, A., Doig, A., Guilliams, T., Latimer, J., McNamee, C., et al. (2019) · Nature Reviews Drug Discovery

Computational Drug Discovery MethodsPharmaceutical Economics and Policy

Cited by 4,718

Systematic identification of genomic markers of drug sensitivity in cancer cells

Garnett, M. J., Edelman, E. J., Heidorn, S. J., Greenman, C. D., Dastur, A., Lau, K. W., Greninger, P., Thompson, I. R., Luo, X., Soares, J., et al. (2012) · Nature

Cancer Genomics and DiagnosticsLung Cancer Treatments and Mutations

Cited by 2,456

Prospective Derivation of a Living Organoid Biobank of Colorectal Cancer Patients

van de Wetering, M., Francies, H. E., Francis, J. M., Bounova, G., Iorio, F., Pronk, A., van Houdt, W., van Gorp, J., Taylor-Weiner, A., Kester, L., et al. (2015) · Cell

Cancer Cells and MetastasisColorectal Cancer Treatments and Studies

Cited by 2,408

A Landscape of Pharmacogenomic Interactions in Cancer

Iorio, F., Knijnenburg, T. A., Vis, D. J., Bignell, G. R., Menden, M. P., Schubert, M., Aben, N., Gonçalves, E., Barthorpe, S., Lightfoot, H., et al. (2016) · Cell

Cancer Genomics and DiagnosticsChromatin Remodeling and Cancer

Cited by 2,090

Prioritization of cancer therapeutic targets using CRISPR–Cas9 screens

Behan, F. M., Iorio, F., Picco, G., Gonçalves, E., Beaver, C. M., Migliardi, G., Santos, R., Rao, Y., Sassi, F., Pinnelli, M., et al. (2019) · Nature

CRISPR and Genetic EngineeringChromatin Remodeling and Cancer

Cited by 1,338

Heterogeneity of genomic evolution and mutational profiles in multiple myeloma

Bolli, N., Avet-Loiseau, H., Wedge, D. C., Van Loo, P., Alexandrov, L. B., Martincorena, I., Dawson, K. J., Iorio, F., Nik-Zainal, S., Bignell, G. R., et al. (2014) · Nature Communications

Multiple Myeloma Research and TreatmentsCancer Genomics and Diagnostics

Cited by 875

Discovery of drug mode of action and drug repositioning from transcriptional responses

Iorio, F., Bosotti, R., Scacheri, E., Belcastro, V., Mithbaokar, P., Ferriero, R., Murino, L., Tagliaferri, R., Brunetti-Pierri, N., Isacchi, A., et al. (2010) · Proceedings of the National Academy of Sciences

Computational Drug Discovery MethodsBioinformatics and Genomic Networks

Cited by 816

A CRISPR Dropout Screen Identifies Genetic Vulnerabilities and Therapeutic Targets in Acute Myeloid Leukemia

Tzelepis, K., Koike-Yusa, H., De Braekeleer, E., Li, Y., Metzakopian, E., Dovey, O. M., Mupo, A., Grinkevich, V., Li, M., Mazan, M., et al. (2016) · Cell Reports

Acute Myeloid Leukemia ResearchChromatin Remodeling and Cancer

Cited by 761

Logic models to predict continuous outputs based on binary inputs with an application to personalized cancer therapy

Knijnenburg, T. A., Klau, G. W., Iorio, F., Garnett, M. J., McDermott, U., Shmulevich, I., & Wessels, L. F. A. (2016) · Scientific Reports

Gene Regulatory Network AnalysisExplainable Artificial Intelligence (XAI)

Cited by 74

Multilevel models Improve precision and Speed of IC 50 Estimates

Vis, D. J., Bombardelli, L., Lightfoot, H., Iorio, F., Garnett, M. J., & Wessels, L. F. (2016) · Pharmacogenomics

Computational Drug Discovery MethodsStatistical Methods and Bayesian Inference

Cited by 71

Computational estimation of quality and clinical relevance of cancer cell lines

Trastulla, L., Noorbakhsh, J., Vazquez, F., McFarland, J., & Iorio, F. (2022) · Molecular Systems Biology

Cancer Genomics and DiagnosticsComputational Drug Discovery Methods

Cited by 31