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Computational models for predicting drug responses in cancer research
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“Yet, unlike with cell lines, high throughput characterization of drug response (i.e., IC50, AUC, etc.) in clinical tumor samples is not feasible29.”
From Exploiting convergent phenotypes to derive a pan-cancer cisplatin response gene expression signature (2023) · cites this paper as [29]
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Sorted by citations
Kadurin, A., Aliper, A., Kazennov, A., Mamoshina, P., Vanhaelen, Q., Khrabrov, K., & Zhavoronkov, A. (2017) · Oncotarget · doi:10.18632/oncotarget.14073 · Cited by 350
Suphavilai, C., Chia, S., Sharma, A., Tu, L., Da Silva, R. P., Mongia, A., DasGupta, R., & Nagarajan, N. (2021) · Genome Medicine · doi:10.1186/s13073-021-01000-y · Cited by 58
Naulaerts, S., Dang, C. C., & Ballester, P. J. (2017) · Oncotarget · doi:10.18632/oncotarget.20923 · Cited by 41
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Exploiting convergent phenotypes to derive a pan-cancer cisplatin response gene expression signature
Scarborough, J. A., Eschrich, S. A., Torres-Roca, J., Dhawan, A., & Scott, J. G. (2023) · npj Precision Oncology · doi:10.1038/s41698-023-00375-y · Cited by 14
Metadata
- Authors
- Azuaje, F.
- Venue
- Briefings in Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2016-07-21
- Pages
- bbw065
- ISSN
- 1467-5463
- 1477-4054
- PMC
- PMC5862310
- Cited by
- 59
- References
- 11
- Language
- en
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