Open Access
| Issue |
BIO Web Conf.
Volume 232, 2026
2026 16th International Conference on Bioscience, Biochemistry and Bioinformatics (ICBBB 2026)
|
|
|---|---|---|
| Article Number | 01001 | |
| Number of page(s) | 11 | |
| Section | Bioinformatics Algorithms and Advanced Omics Data Analysis | |
| DOI | https://doi.org/10.1051/bioconf/202623201001 | |
| Published online | 24 April 2026 | |
- Q. Gao et al., "Opportunities and challenges for co-delivery nanomedicines based on combination of phytochemicals with chemotherapeutic drugs in cancer treatment," Advanced drug delivery reviews, vol. 188, p. 114445, 2022. [Google Scholar]
- R. L. Siegel, T. B. Kratzer, A. N. Giaquinto, H. Sung, and A. Jemal, "Cancer statistics, 2025," Ca, vol. 75, no. 1, p. 10, 2025. [Google Scholar]
- R. L. Siegel, A. N. Giaquinto, and A. Jemal, "Cancer statistics, 2024," CA: a cancer journal for clinicians, vol. 74, no. 1, pp. 12–49, 2024. [Google Scholar]
- C. Zhang, C. Xu, X. Gao, and Q. Yao, "Platinum-based drugs for cancer therapy and anti-tumor strategies," Theranostics, vol. 12, no. 5, p. 2115, 2022. [Google Scholar]
- F. Xia et al., "A cross-study analysis of drug response prediction in cancer cell lines," Briefings in bioinformatics, vol. 23, no. 1, p. bbab356, 2022. [Google Scholar]
- T. Das, K. Bhattarai, S. Rajaganapathy, L. Wang, J. R. Cerhan, and N. Zong, "Leveraging multi-source to resolve inconsistency across pharmacogenomic datasets in drug sensitivity prediction," medRxiv, 2023. [Google Scholar]
- S. Ha, J. Park, and K. Jo, "Comparative analysis of regression algorithms for drug response prediction using GDSC dataset," BMC Research Notes, vol. 18, no. Suppl 1, p. 10, 2025. [Google Scholar]
- G. o. D. S. i. Cancer. "Genomics of Drug Sensitivity in Cancer." https://www.cancerrxgene.org/news (accessed June 14, 2025). [Google Scholar]
- A. Park, Y. Lee, and S. Nam, "A performance evaluation of drug response prediction models for individual drugs," Scientific Reports, vol. 13, no. 1, p. 11911, 2023. [Google Scholar]
- A. Park et al., "A comprehensive evaluation of regression-based drug responsiveness prediction models, using cell viability inhibitory concentrations (IC50 values)," Bioinformatics, vol. 38, no. 10, pp. 2810–2817, 2022. [Google Scholar]
- R. Arafeh, T. Shibue, J. M. Dempster, W. C. Hahn, and F. Vazquez, "The present and future of the Cancer Dependency Map," Nature Reviews Cancer, vol. 25, no. 1, pp. 59–73, 2025. [Google Scholar]
- P. Du, H. Huai, X. Wu, H. Wang, W. Liu, and X. Tang, "Using XGBoost-SHAP for understanding the ecosystem services trade-off effects and driving mechanisms in ecologically fragile areas," Frontiers in Plant Science, vol. 16, p. 1552818, 2025. [Google Scholar]
- M. H. Cohen et al., "Approval summary: nelarabine for the treatment of T-cell lymphoblastic leukemia/lymphoma," Clinical cancer research, vol. 12, no. 18, pp. 5329–5335, 2006. [Google Scholar]
- !!! INVALID CITATION !!! [Google Scholar]
- S. Stilgenbauer et al., "Venetoclax for patients with chronic lymphocytic leukemia with 17p deletion: results from the full population of a phase II pivotal trial," Journal of Clinical Oncology, vol. 36, no. 19, pp. 1973–1980, 2018. [Google Scholar]
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