Machine learning approaches to drug response prediction: challenges and recent progress G Adam, L Rampášek, Z Safikhani, P Smirnov, B Haibe-Kains, ... NPJ precision oncology 4 (1), 19, 2020 | 267 | 2020 |
PharmacoGx: an R package for analysis of large pharmacogenomic datasets P Smirnov, Z Safikhani, N El-Hachem, D Wang, A She, C Olsen, ... Bioinformatics 32 (8), 1244-1246, 2016 | 267 | 2016 |
Dr. VAE: improving drug response prediction via modeling of drug perturbation effects L Rampášek, D Hidru, P Smirnov, B Haibe-Kains, A Goldenberg Bioinformatics 35 (19), 3743-3751, 2019 | 181* | 2019 |
PharmacoDB: an integrative database for mining in vitro anticancer drug screening studies P Smirnov, V Kofia, A Maru, M Freeman, C Ho, N El-Hachem, GA Adam, ... Nucleic acids research 46 (D1), D994-D1002, 2018 | 145 | 2018 |
Revisiting inconsistency in large pharmacogenomic studies Z Safikhani, P Smirnov, M Freeman, N El-Hachem, A She, Q Rene, ... F1000Research 5, 2016 | 119 | 2016 |
Disruption of the anaphase-promoting complex confers resistance to TTK inhibitors in triple-negative breast cancer KL Thu, J Silvester, MJ Elliott, W Ba-Alawi, MH Duncan, AC Elia, AS Mer, ... Proceedings of the National Academy of Sciences 115 (7), E1570-E1577, 2018 | 72 | 2018 |
Gene isoforms as expression-based biomarkers predictive of drug response in vitro Z Safikhani, P Smirnov, KL Thu, J Silvester, N El-Hachem, R Quevedo, ... Nature communications 8 (1), 1126, 2017 | 72 | 2017 |
Integrative pharmacogenomics analysis of patient-derived xenografts AS Mer, W Ba-Alawi, P Smirnov, YX Wang, B Brew, J Ortmann, MS Tsao, ... Cancer research 79 (17), 4539-4550, 2019 | 46 | 2019 |
Integrative cancer pharmacogenomics to infer large-scale drug taxonomy N El-Hachem, DMA Gendoo, LS Ghoraie, Z Safikhani, P Smirnov, ... Cancer research 77 (11), 3057-3069, 2017 | 42 | 2017 |
Assessment of pharmacogenomic agreement Z Safikhani, N El-Hachem, R Quevedo, P Smirnov, A Goldenberg, ... F1000Research 5, 2016 | 38 | 2016 |
Drug sensitivity prediction from cell line-based pharmacogenomics data: guidelines for developing machine learning models H Sharifi-Noghabi, S Jahangiri-Tazehkand, P Smirnov, C Hon, ... Briefings in Bioinformatics 22 (6), bbab294, 2021 | 36 | 2021 |
Modeling cellular response in large-scale radiogenomic databases to advance precision radiotherapy VSK Manem, M Lambie, I Smith, P Smirnov, V Kofia, M Freeman, ... Cancer research 79 (24), 6227-6237, 2019 | 31 | 2019 |
The mevalonate pathway is an actionable vulnerability of t(4;14)-positive multiple myeloma J Longo, P Smirnov, Z Li, E Branchard, JE van Leeuwen, JD Licht, ... Leukemia 35 (3), 796-808, 2021 | 28 | 2021 |
Orchestrating and sharing large multimodal data for transparent and reproducible research A Mammoliti, P Smirnov, M Nakano, Z Safikhani, C Eeles, H Seo, SK Nair, ... Nature communications 12 (1), 5797, 2021 | 25* | 2021 |
Assessment of genetic drift in large pharmacogenomic studies R Quevedo, P Smirnov, D Tkachuk, C Ho, N El-Hachem, Z Safikhani, ... Cell systems 11 (4), 393-401. e2, 2020 | 23 | 2020 |
Tissue specificity of in vitro drug sensitivity F Yao, SA Madani Tonekaboni, Z Safikhani, P Smirnov, N El-Hachem, ... Journal of the American Medical Informatics Association 25 (2), 158-166, 2018 | 23 | 2018 |
Safikhani et al. reply Z Safikhani, N El-Hachem, P Smirnov, M Freeman, A Goldenberg, ... Nature 540 (7631), E2-E4, 2016 | 23 | 2016 |
PharmacoDB 2.0: improving scalability and transparency of in vitro pharmacogenomics analysis N Feizi, SK Nair, P Smirnov, G Beri, C Eeles, PN Esfahani, M Nakano, ... Nucleic acids research 50 (D1), D1348-D1357, 2022 | 18 | 2022 |
ToxicoDB: an integrated database to mine and visualize large-scale toxicogenomic datasets SK Nair, C Eeles, C Ho, G Beri, E Yoo, D Tkachuk, A Tang, P Nijrabi, ... Nucleic acids research 48 (W1), W455-W462, 2020 | 14 | 2020 |
Stochastic combinatorial ensembles for defending against adversarial examples GA Adam, P Smirnov, D Duvenaud, B Haibe-Kains, A Goldenberg arXiv preprint arXiv:1808.06645, 2018 | 13 | 2018 |