Articles dans des revues avec comité de lecture (42)
  1. 25. Bareche, Y., Venet, D., Ignatiadis, M., Aftimos, P., Piccart-Gebhart, M., Rothé, F., & Sotiriou, C. (2018). Unravelling triple-negative breast cancer molecular heterogeneity using an integrative multiomic analysis. Annals of oncology. doi:10.1093/annonc/mdy024
  2. 26. Hanker, A. A., Frampton, G. G., Sanford, E., Owens, P., Becker, J., Groseclose, R. M., Castellino, S., Joensuu, H., Huober, J., Brase, J. C., Majjaj, S., Garrett, J. J., Brohée, S., Venet, D., Brown, D. N., Baselga, J., Piccart-Gebhart, M., Sotiriou, C., Arteaga, C. C., Estrada, M. V., Moore, P. P., Ericsson, P. G., Koch, J. J., Langley, E., Singh, S., & Kim, P. P. (2017). HER2-overexpressing breast cancers amplify FGFR signaling upon acquisition of resistance to dual therapeutic blockade of HER2. Clinical cancer research, 23(15), 4323-4334. doi:10.1158/1078-0432.CCR-16-2287
  3. 27. Fumagalli, D., Venet, D., Ignatiadis, M., Abdel Azim, H. H., Maetens, M. M., Rothé, F., Salgado, R., Bradbury, I., Pusztai, L., Harbeck, N., Gomez, H., Chang, T.-W., Coccia-Portugal, M., Di Cosimo, S., de Azambuja, E., de la Peña, L., Nuciforo, P., Brase, J. J., Huober, J., Baselga, J., Piccart-Gebhart, M., Loi, S., & Sotiriou, C. (2017). RNA Sequencing to Predict Response to Neoadjuvant Anti-HER2 Therapy: A Secondary Analysis of the NeoALTTO Randomized Clinical Trial. JAMA oncology, 3(2), 227-234. doi:10.1001/jamaoncol.2016.3824
  4. 28. Desmet, L., Venet, D., Doffagne, E., Timmermans, C., Legrand, C., Burzykowski, T., & Buyse, M. (2017). Use of the Beta-Binomial Model for Central Statistical Monitoring of Multicenter Clinical Trials. Statistics in Biopharmaceutical Research, 9(1), 1-11. doi:10.1080/19466315.2016.1164751
  5. 29. Timmermans, C., Venet, D., & Burzykowski, T. (2016). Data-driven risk identification in phase III clinical trials using central statistical monitoring. International journal of clinical oncology, 21(1), 38-45. doi:10.1007/s10147-015-0877-5
  6. 30. Timmermans, C., Doffagne, E., Venet, D., Desmet, L., Legrand, C., Burzykowski, T., & Buyse, M. (2016). Statistical monitoring of data quality and consistency in the Stomach Cancer Adjuvant Multi-institutional Trial Group Trial. Gastric Cancer, 19(1), 24-30. doi:10.1007/s10120-015-0533-9
  7. 31. Sonnenblick, A., Desmedt, C., Neven, P., Loibl, S., Denkert, C., Joensuu, H., Loi, S., Sirtaine, N., Kellokumpu-Lehtinen, P.-L., Piccart-Gebhart, M., Sotiriou, C., Brohée, S., Fumagalli, D., Vincent, D., Venet, D., Ignatiadis, M., Salgado, R., Van den Eynden, G., & Rothé, F. (2015). Constitutive phosphorylated STAT3-associated gene signature is predictive for trastuzumab resistance in primary HER2-positive breast cancer. BMC medicine, 13(1), 177. doi:10.1186/s12916-015-0416-2
  8. 32. Desmet, L., Legrand, C., Venet, D., Doffagne, E., Timmermans, C., Burzykowski, T., & Buyse, M. (2014). Linear mixed-effects models for central statistical monitoring of multicenter clinical trials. Statistics in medicine, 33(30), 5265-5279. doi:10.1002/sim.6294
  9. 33. Venet, D., Doffagne, E., Burzykowski, T., Beckers, F., Tellier, Y., Genevois-Marlin, E., Becker, U., Bee, V., Wilson, V., Legrand, C., & Buyse, M. (2012). A statistical approach to central monitoring of data quality in clinical trials. Clinical trials, 9(6), 705-713. doi:10.1177/1740774512447898
  10. 34. Coletta, A., Molter, C., Duque, R., Steenhoff, D., Taminau, J., De Schaetzen, V., Meganck, S., Lazar, C., Venet, D., Detours, V., Nowe, A., Bersini, H., & Weiss Solis, D. Y. (2012). InSilico DB genomic datasets hub: an efficient starting point for analyzing genome-wide studies in GenePattern, Integrative Genomics Viewer, and R/Bioconductor. Genome biology, 13(11), R104. doi:10.1186/gb-2012-13-11-r104
  11. 35. Venet, D., Detours, V., & Bersini, H. (2012). A Measure of the Signal-to-Noise Ratio of Microarray Samples and Studies Using Gene Correlations. PloS one, 7(12), e51013. doi:10.1371/journal.pone.0051013
  12. 36. Venet, D., Dumont, J. E., & Detours, V. (2011). Most random gene expression signatures are significantly associated with breast cancer outcome. PLoS computational biology, 7(10), e1002240. doi:10.1371/journal.pcbi.1002240
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