https://orcid.org/0000-0001-8423-1823
References
- Alonso, A., T. Bigirumurame, T. Burzykowski, M. Buyse, G. Molenberghs, L. Muchene, N. Perualila, Z. Shkedy, and W. Van der Elst. 2017. Applied surrogate endpoint evaluation methods with SAS and R. CRC Press.
- Analytics, R., and S. Weston. 2015. foreach: Provides Foreach Looping Construct for R. R Package Version 1.4.3.https://rdrr.io/github/cloudcell/foreach/.
- Azimfar, S. J. 2015. Efficiency of parallel processing in multi-core processors. Cumhuriyet Science Journal 36(4):2535-2543. Special Issue: Technological Advances of Engineering Sciences. https://pdfs.semanticscholar.org/d7b4/b9d01432c0607c2ae96d3fb634692f21c845.pdf
- Benjamini, Y., and Y. Hochberg. 1995. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society. Series B (methodological) 57:289–300. doi:10.1111/rssb.1995.57.issue-1.
- Bex, G. J. Worker framework. Online user guideline. https://vlaams-supercomputing-centrum-vscdocumentation.readthedocs-hosted.com/en/latest/jobs/worker_framework.html.
- Burzykowski, T., G. Molenberghs, and M. Buyse. 2004. The validation of surrogate end points by using data from randomized clinical trials: a case-study in advanced colorectal cancer. Journal of the Royal Statistical Society: Series A (statistics in Society) 103–124. doi:10.1111/j.1467-985X.2004.00293.x.
- Buyse, M., and G. Molenberghs. 1998. The validation of surrogate endpoints in randomized experiments. Biometrics 54:186–201. doi:10.2307/2533853.
- Perualila, N. J., Z. Shkedy, R. Sengupta, T. Bigirumurame, L. Bijnens, W. Talloen, B. Verbist, H. W. Göhlmann, A. Kasim, and Q. S. T. A. R. Consortium. 2016b. Applied surrogate endpoint evaluation methods with SAS and R. CRC Press, 275–309. chapter High-dimensional biomarkers in drug discovery: the QSTAR framework.
- Perualila-Tan, N. J., A. Kasim, W. Talloen, B. Verbist, H. W. Göhlmann, Q. S. T. A. R. Consortium, and Z. Shkedy. 2016a. A joint modeling approach for uncovering associations between gene expression, bioactivity and chemical structure in early drug discovery to guide lead selection and genomic biomarker development. Statistical Applications in Genetics and Molecular Biology 15:291–304. doi:10.1515/sagmb-2014-0086.
- Sahni, S., and G. Vairaktarakis. 1996. The master-slave paradigm in parallel computer and industrial settings. Journal of Global Optimization 9:357–377. doi:10.1007/BF00121679.
- Schmidberger, M., M. Morgan, D. Eddelbuettel, H. Yu, L. Tierney, and U. Mansmann. 2009. State of the art in parallel computing with R. Journal of Statistical Software 31: doi: 10.18637/jss.v031.i01.
- Steve, W. 2012. Using the foreach package. Online user manual. https://cran.r-project.org/web/packages/foreach/vignettes/foreach.pdf.
- Team, R. C. 2016. R: A language and environment for statistical computing.
- Vera, G., R. Jansen, and R. Suppi. 2008. R/parallel - speeding up bioinformatics analysis with r. BMC Bioinformatics 390. doi:10.1186/1471-2105-9-390.
- Verbist, B., G. Klambauer, L. Vervoort, W. Talloen, Q. Consortium, Z. Shkedy, O. Thas, A. Bender, H. Göhlmann, and S. Hochreiter. 2015. Using transcriptomics to guide lead optimization in drug discovery projects: lessons learned from the qstar project. Drug Discovery Today 20 (5):505–513. doi:10.1016/j.drudis.2014.12.014.
- Woodburn, J. 1999. The epidermal growth factor receptor and its inhibition in cancer therapy. Pharmacology & Therapeutics 822 (2–3):241–250. doi:10.1016/S0163-7258(98)00045-X.