Precision and recall oncology: combining multiple gene mutations for improved identification of drug-sensitive tumours
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// Stefan Naulaerts 1, 2, 3, 4 , Cuong C. Dang 5 and Pedro J. Ballester 1, 2, 3, 4 1 Computational Biology and Drug Design, Cancer Research Center of Marseille, INSERM U1068, Marseille, France 2 Institut Paoli-Calmettes, Marseille, France 3 Aix-Marseille Université, Marseille, France 4 CNRS UMR7258, Marseille, France 5 Faculty of Information Technology, VNU University of Engineering and Technology, Hanoi, Vietnam Correspondence to: Pedro J. Ballester, email: pedro.ballester@inserm.fr Keywords: biomarker discovery, machine learning, drug sensitivity, genomics, cancer Received: March 16, 2017 Accepted: August 14, 2017 Published: September 15, 2017 ABSTRACT Cancer drug therapies are only effective in a small proportion of patients. To make things worse, our ability to identify these responsive patients before administering a treatment is generally very limited. The recent arrival of large-scale pharmacogenomic data sets, which measure the sensitivity of molecularly profiled cancer cell lines to a panel of drugs, has boosted research on the discovery of drug sensitivity markers. However, no systematic comparison of widely-used single-gene markers with multi-gene machine-learning markers exploiting genomic data has been so far conducted. We therefore assessed the performance offered by these two types of models in discriminating between sensitive and resistant cell lines to a given drug. This was carried out for each of 127 considered drugs using genomic data characterising the cell lines. We found that the proportion of cell lines predicted to be sensitive that are actually sensitive (precision) varies strongly with the drug and type of model used. Furthermore, the proportion of sensitive cell lines that are correctly predicted as sensitive (recall) of the best single-gene marker was lower than that of the multi-gene marker in 118 of the 127 tested drugs. We conclude that single-gene markers are only able to identify those drug-sensitive cell lines with the considered actionable mutation, unlike multi-gene markers that can in principle combine multiple gene mutations to identify additional sensitive cell lines. We also found that cell line sensitivities to some drugs (e.g. Temsirolimus, 17-AAG or Methotrexate) are better predicted by these machine-learning models.
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“The effectiveness of MCC has been shown in other scientific fields as well [43, 44].”
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Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538 · see citing works
The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity
Barretina, J., Caponigro, G., Stransky, N., Venkatesan, K., Margolin, A. A., Kim, S., Wilson, C. J., Lehár, J., Kryukov, G. V., Sonkin, D., et al. (2012) · Nature · doi:10.1038/nature11003 · Cited by 7,950 · see citing works
Comparison of the predicted and observed secondary structure of T4 phage lysozyme
Matthews, B. W. (1975) · Biochimica et Biophysica Acta (BBA) - Protein Structure · doi:10.1016/0005-2795(75)90109-9 · Cited by 5,399 · cited 1× · see citing works
“From this contingency table, the discrimination offered by a classifier can be summarised by the Matthews Correlation Coefficient (MCC) [62].”
MethodStatistical Modeling: The Two Cultures (with comments and a rejoinder by the author)
Breiman, L. (2001) · Statistical Science · doi:10.1214/ss/1009213726 · Cited by 4,340 · see citing works
Yang, W., Soares, J., Greninger, P., Edelman, E. J., Lightfoot, H., Forbes, S., Bindal, N., Beare, D., Smith, J. A., Thompson, I. R., et al. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks1111 · Cited by 4,051 · see citing works
Hawkins, D. M. (2004) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci0342472 · Cited by 2,522 · see citing works
Bias in error estimation when using cross-validation for model selection
Varma, S., & Simon, R. (2006) · BMC Bioinformatics · doi:10.1186/1471-2105-7-91 · Cited by 2,485 · cited 1× · see citing works
“First, both model selection and performance estimation have been carried out using the same data folds, which is known to overestimate performance [29, 43].”
MethodSystematic identification of genomic markers of drug sensitivity in cancer cells
Garnett, M. J., Edelman, E. J., Heidorn, S. J., Greenman, C. D., Dastur, A., Lau, K. W., Greninger, P., Thompson, I. R., Luo, X., Soares, J., et al. (2012) · Nature · doi:10.1038/nature11005 · Cited by 2,456 · see citing works
Johnstone, R. W., Ruefli, A. A., & Lowe, S. W. (2002) · Cell · doi:10.1016/s0092-8674(02)00625-6 · Cited by 2,236 · see citing works
QSAR Modeling: Where have you been? Where are you going to?
Cherkasov, A., Muratov, E. N., Fourches, D., Varnek, A., Baskin, I. I., Cronin, M., Dearden, J., Gramatica, P., Martin, Y. C., Todeschini, R., et al. (2014) · Journal of Medicinal Chemistry · doi:10.1021/jm4004285 · Cited by 2,164 · see citing works
Machine learning applications in genetics and genomics
Libbrecht, M. W., & Noble, W. S. (2015) · Nature Reviews Genetics · doi:10.1038/nrg3920 · Cited by 2,146 · see citing works
A Landscape of Pharmacogenomic Interactions in Cancer
Iorio, F., Knijnenburg, T. A., Vis, D. J., Bignell, G. R., Menden, M. P., Schubert, M., Aben, N., Gonçalves, E., Barthorpe, S., Lightfoot, H., et al. (2016) · Cell · doi:10.1016/j.cell.2016.06.017 · Cited by 2,090 · see citing works
(2010) · doi:10.5555/1756006.1859921 · Cited by 1,924 · see citing works
Ligand-targeted therapeutics in anticancer therapy
Allen, T. M. (2002) · Nature reviews. Cancer · doi:10.1038/nrc903 · Cited by 1,648 · see citing works
The Reactome pathway Knowledgebase
Fabregat, A., Sidiropoulos, K., Garapati, P., Gillespie, M., Hausmann, K., Haw, R., Jassal, B., Jupe, S., Korninger, F., McKay, S., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv1351 · Cited by 1,201 · see citing works
Combinatorial drug therapy for cancer in the post-genomic era
Al-Lazikani, B., Banerji, U., & Workman, P. (2012) · Nature Biotechnology · doi:10.1038/nbt.2284 · Cited by 1,070 · see citing works
Ballester, P. J., & Mitchell, J. B. O. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq112 · Cited by 988 · see citing works
Self-citationRandom forests for genomic data analysis
Chen, X., & Ishwaran, H. (2012) · Genomics · doi:10.1016/j.ygeno.2012.04.003 · Cited by 956 · see citing works
Geeleher, P., Cox, N. J., & Huang, R. S. (2014) · Genome biology · doi:10.1186/gb-2014-15-3-r47 · Cited by 889 · see citing works
Automated design of ligands to polypharmacological profiles
Besnard, J., Ruda, G. F., Setola, V., Abecassis, K., Rodriguiz, R. M., Huang, X.-P., Norval, S., Sassano, M. F., Shin, A. I., Webster, L. A., et al. (2012) · Nature · doi:10.1038/nature11691 · Cited by 832 · see citing works
Harnessing Connectivity in a Large-Scale Small-Molecule Sensitivity Dataset
Seashore-Ludlow, B., Rees, M. G., Cheah, J. H., Cokol, M., Price, E. V., Coletti, M. E., Jones, V., Bodycombe, N. E., Soule, C. K., Gould, J., et al. (2015) · Cancer Discovery · doi:10.1158/2159-8290.cd-15-0235 · Cited by 813 · see citing works
A community effort to assess and improve drug sensitivity prediction algorithms
Community, N. D., Costello, J. C., Heiser, L. M., Georgii, E., Gönen, M., Menden, M. P., Wang, N. J., Bansal, M., Ammad-ud-din, M., Hintsanen, P., et al. (2014) · Nature Biotechnology · doi:10.1038/nbt.2877 · Cited by 761 · see citing works
Model selection and overfitting
Lever, J., Krzywinski, M. I., & Altman, N. (2016) · Nature Methods · doi:10.1038/nmeth.3968 · Cited by 727 · see citing works
From big data analysis to personalized medicine for all: challenges and opportunities
Alyass, A., Turcotte, M., & Meyre, D. (2015) · BMC Medical Genomics · doi:10.1186/s12920-015-0108-y · Cited by 557 · see citing works
Menden, M. P., Iorio, F., Garnett, M. J., McDermott, U., Benes, C. H., Ballester, P. J., & Sáez-Rodríguez, J. (2013) · PLoS ONE · doi:10.1371/journal.pone.0061318 · Cited by 543 · see citing works
Self-citationMolecular genetic testing and the future of clinical genomics
Huston, S., & Katsanis, N. (2013) · Nature Reviews Genetics · doi:10.1038/nrg3493 · Cited by 422 · see citing works
Lever, J., Krzywinski, M. I., & Altman, N. (2016) · Nature Methods · doi:10.1038/nmeth.3945 · Cited by 417 · see citing works
Kadurin, A., Aliper, A., Kazennov, A., Mamoshina, P., Vanhaelen, Q., Khrabrov, K., & Zhavoronkov, A. (2017) · Oncotarget · doi:10.18632/oncotarget.14073 · Cited by 350 · see citing works
Holbeck, S. L., Collins, J. M., & Doroshow, J. H. (2010) · Molecular Cancer Therapeutics · doi:10.1158/1535-7163.mct-10-0106 · Cited by 312 · see citing works
Majumder, B., Ulaganathan, B., Thiyagarajan, S., Radhakrishnan, P., Narasimhan, H., Dhandapani, M., Brijwani, N., Pinto, D. D., Prasath, A., Shanthappa, B., et al. (2015) · Nature Communications · doi:10.1038/ncomms7169 · Cited by 307 · see citing works
Implementing personalized cancer genomics in clinical trials
Simon, R. M., & Roychowdhury, S. (2013) · Nature Reviews Drug Discovery · doi:10.1038/nrd3979 · Cited by 302 · see citing works
Molecularly targeted cancer therapy: some lessons from the past decade
Huang, M., Shen, A., Ding, J., & Geng, M. (2013) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2013.11.004 · Cited by 289 · see citing works
The exomes of the NCI-60 panel: a genomic resource for cancer biology and systems pharmacology
Abaan, O. D., Polley, E. C., Davis, S. R., Zhu, Y. J., Bilke, S., Walker, R. L., Pineda, M., Gindin, Y., Jiang, Y., Reinhold, W. C., et al. (2013) · Cancer Research · doi:10.1158/0008-5472.can-12-3342 · Cited by 288 · see citing works
Basket Trials and the Evolution of Clinical Trial Design in an Era of Genomic Medicine
Redig, A. J., & Janne, P. A. (2015) · Journal of Clinical Oncology · doi:10.1200/jco.2014.59.8433 · Cited by 281 · see citing works
Li, H., Leung, K., Wong, M., & Ballester, P. J. (2015) · Molecular Informatics · doi:10.1002/minf.201400132 · Cited by 252 · cited 2× · see citing works
“Moreover, we provided an intuitive example of an analytical model robust to overfitting (see section 2 of the supplementary information of this paper [45]).”
BackgroundMethodSelf-citationSecretome proteomics for discovery of cancer biomarkers
Makridakis, M., & Vlahou, A. (2010) · Journal of Proteomics · doi:10.1016/j.jprot.2010.07.001 · Cited by 250 · see citing works
Cancer pharmacogenomics: strategies and challenges
Wheeler, H. E., Maitland, M. L., Dolan, M. E., Cox, N. J., & Ratain, M. J. (2012) · Nature Reviews Genetics · doi:10.1038/nrg3352 · Cited by 215 · see citing works
Cancer Genomics: Technology, Discovery, and Translation
Tran, B., Dancey, J. E., Kamel‐Reid, S., McPherson, J. D., Bedard, P. L., Brown, A. M. K., Zhang, T., Shaw, P. H., Onetto-Pothier, N., Stein, L. D., et al. (2012) · Journal of Clinical Oncology · doi:10.1200/jco.2011.39.2316 · Cited by 187 · see citing works
Williams, S. A., Anderson, W. C., Santaguida, M. T., & Dylla, S. J. (2013) · Laboratory Investigation · doi:10.1038/labinvest.2013.92 · Cited by 185 · see citing works
Pragmatic issues in biomarker evaluation for targeted therapies in cancer
Gramont, A. D. E., Watson, S., Ellis, L. M., Rodon, J. A., Tabernero, J. M., de Gramont, A., & Hamilton, S. R. (2014) · Nature Reviews Clinical Oncology · doi:10.1038/nrclinonc.2014.202 · Cited by 174 · see citing works
Predicting in vitro drug sensitivity using Random Forests
Riddick, G., Song, H., Ahn, S. J., Walling, J., Borges-Rivera, D., Zhang, W., & Fine, H. A. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq628 · Cited by 170 · see citing works
Computational models for predicting drug responses in cancer research
Azuaje, F. J. (2016) · Briefings in Bioinformatics · doi:10.1093/bib/bbw065 · Cited by 162 · see citing works
Regularized Machine Learning in the Genetic Prediction of Complex Traits
Okser, S., Pahikkala, T., Airola, A., Salakoski, T., Ripatti, S., & Aittokallio, T. A. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004754 · Cited by 156 · see citing works
Cancer Pharmacogenomics: Early Promise, But Concerted Effort Needed
McLeod, H. L. (2013) · Science · doi:10.1126/science.1234139 · Cited by 156 · see citing works
Improved large-scale prediction of growth inhibition patterns using the NCI60 cancer cell line panel
Cortés‐Ciriano, I., van Westen, G. J. P., Bouvier, G., Nilges, M., Overington, J. P., Bender, A., & Malliavin, T. E. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv529 · Cited by 121 · see citing works
Integrative and Personalized QSAR Analysis in Cancer by Kernelized Bayesian Matrix Factorization
Ammad-ud-din, M., Georgii, E., Gönen, M., Laitinen, T., Kallioniemi, O., Wennerberg, K., Poso, A., & Samuel Kaski, A. (2014) · Journal of Chemical Information and Modeling · doi:10.1021/ci500152b · Cited by 114 · see citing works
Weinstein, J. N. (2012) · Nature · doi:10.1038/483544a · Cited by 107 · see citing works
Single-cell mutational profiling and clonal phylogeny in cancer
Potter, N. E., Ermini, L., Papaemmanuil, E., Cazzaniga, G., Vijayaraghavan, G., Titley, I., Ford, A., Campbell, P., Kearney, L., & Greaves, M. (2013) · Genome Research · doi:10.1101/gr.159913.113 · Cited by 107 · see citing works
Peón, A., Naulaerts, S., & Ballester, P. J. (2017) · Scientific Reports · doi:10.1038/s41598-017-04264-w · Cited by 74 · see citing works
Self-citationKnijnenburg, T. A., Klau, G. W., Iorio, F., Garnett, M. J., McDermott, U., Shmulevich, I., & Wessels, L. F. A. (2016) · Scientific Reports · doi:10.1038/srep36812 · Cited by 74 · see citing works
Ahmed, J., Meinel, T., Dunkel, M., Murgueitio, M. S., Adams, R. A., Blasse, C., Eckert, A. P., Preißner, S., & Preißner, R. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq910 · Cited by 71 · see citing works
Agostini, M., Janssen, K., Kim, L., D’Angelo, E., Pizzini, S., Zangrando, A., Zanon, C., Pastrello, C., Maretto, I., Digito, M., et al. (2015) · Oncotarget · doi:10.18632/oncotarget.4935 · Cited by 46 · see citing works
Toward better benchmarking: challenge-based methods assessment in cancer genomics
Boutros, P. C., Margolin, A. A., Stuart, J. M., Califano, A., & Stolovitzky, G. A. (2014) · Genome biology · doi:10.1186/s13059-014-0462-7 · Cited by 42 · see citing works
Discrepancies in drug sensitivity
Weinstein, J. N., & Lorenzi, P. L. (2013) · Nature · doi:10.1038/nature12839 · Cited by 42 · see citing works
Personalized Chemotherapy Profiling Using Cancer Cell Lines from Selectable Mice
Kamiyama, H., Rauenzahn, S. L., Shim, J. S., Karikari, C. A., Feldmann, G., Hua, L., Kamiyama, M., Schuler, F. W., Lin, M., Beaty, R. M., et al. (2013) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-12-2127 · Cited by 31 · see citing works
Nguyen, L. C., Dang, C. C., & Ballester, P. J. (2016) · F1000Research · doi:10.12688/f1000research.10529.2 · Cited by 29 · see citing works
Self-citationCheng, T., Wang, Y., & Bryant, S. H. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq550 · Cited by 27 · see citing works
Designing of promiscuous inhibitors against pancreatic cancer cell lines
Kumar, R., Chaudhary, K., Singla, D., Gautam, A., & Raghava, G. P. S. (2014) · Scientific Reports · doi:10.1038/srep04668 · Cited by 21 · see citing works
Kim, S., Sundaresan, V., Zhou, L. J., & Kahveci, T. (2016) · PLoS ONE · doi:10.1371/journal.pone.0162173 · Cited by 21 · see citing works
Genomics of drug sensitivity in cancer
Heidorn, S. J. (2013) · Cited by 9 · see citing works
Füllbeck, M., Dunkel, M., Hossbach, J., Daniel, P. T., & Preißner, R. (2009) · Chemical Biology & Drug Design · doi:10.1111/j.1747-0285.2009.00883.x · Cited by 7 · see citing works
Flagged first, then by citations
1 of the 64 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Chicco, D., & Jurman, G. (2020) · BMC Genomics · doi:10.1186/s12864-019-6413-7 · Cited by 6,143 · see citing works
Deep learning for drug response prediction in cancer
Baptista, D., Ferreira, P. G., & Rocha, M. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bbz171 · Cited by 239 · see citing works
Predicting Synergism of Cancer Drug Combinations Using NCI-ALMANAC Data
Sidorov, P., Naulaerts, S., Ariey-Bonnet, J., Pasquier, E., & Ballester, P. J. (2019) · Frontiers in Chemistry · doi:10.3389/fchem.2019.00509 · Cited by 151 · see citing works
Self-citationLind, A. P., & Anderson, P. C. (2019) · PLoS ONE · doi:10.1371/journal.pone.0219774 · Cited by 141 · see citing works
A review of model evaluation metrics for machine learning in genetics and genomics
Miller, C., Portlock, T., Nyaga, D. M., & O'Sullivan, J. M. (2024) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2024.1457619 · Cited by 158 · see citing works
Piñeiro-Yáñez, E., Reboiro‐Jato, M., Goméz-López, G., Perales-Patón, J., Troulé, K., Rodrı́guez, J. M., Tejero, H., Shimamura, T., Lopez-Casas, P. P., Carretero, J., et al. (2018) · Genome Medicine · doi:10.1186/s13073-018-0546-1 · Cited by 101 · see citing works
Diallo, R., Edalo, C., & Awe, O. O. (2024) · STEAM-H: Science, Technology, Engineering, Agriculture, Mathematics & Health · doi:10.1007/978-3-031-72215-8_12 · Cited by 107 · see citing works
Tolios, A., Rivas, J. D. L., Hovig, E. J., Trouillas, P., Scorilas, A., & Mohr, T. T. (2019) · Drug Resistance Updates · doi:10.1016/j.drup.2019.100662 · Cited by 63 · see citing works
Bomane, A., Gonçalvès, A., & Ballester, P. J. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.01041 · Cited by 64 · see citing works
Self-citationCortés‐Ciriano, I., & Bender, A. (2019) · Journal of Cheminformatics · doi:10.1186/s13321-019-0364-5 · Cited by 63 · see citing works
Ogunleye, A. Z., Piyawajanusorn, C., Gonçalvès, A., Ghislat, G., & Ballester, P. J. (2022) · Advanced Science · doi:10.1002/advs.202201501 · Cited by 43 · see citing works
Self-citationCortés‐Ciriano, I., & Bender, A. (2019) · Apollo (University of Cambridge) · doi:10.17863/cam.43757 · Cited by 39 · see citing works
Bueschbell, B., Caniceiro, A. B., Suzano, P. M. S., Machuqueiro, M., Rosário‐Ferreira, N., & Moreira, I. S. (2022) · Drug Resistance Updates · doi:10.1016/j.drup.2022.100811 · Cited by 37 · see citing works
Park, S., Soh, J., & Lee, H. (2021) · BMC Bioinformatics · doi:10.1186/s12859-021-04146-z · Cited by 33 · see citing works
Nguyen, L. C., Naulaerts, S., Bomane, A., Bruna, A., Ghislat, G., & Ballester, P. J. (2021) · Biomedicines · doi:10.3390/biomedicines9101319 · Cited by 36 · see citing works
Self-citationPredicting the formation of NADES using a transformer-based model
Ayres, L. B., Gómez, F. J. V., Silva, M. F., Linton, J. R., & García, C. D. (2024) · Scientific Reports · doi:10.1038/s41598-022-27106-w · Cited by 27 · see citing works
OncoNetExplainer: Explainable Predictions of Cancer Types Based on Gene Expression Data
Karim, M. R., Cochez, M., Beyan, O., Decker, S., & Lange, C. (2019) · IEEE International Conference on Bioinformatics and Bioengineering (BIBE) · doi:10.1109/bibe.2019.00081 · Cited by 26 · see citing works
Naulaerts, S., Menden, M. P., & Ballester, P. J. (2020) · Biomolecules · doi:10.3390/biom10060963 · Cited by 24 · see citing works
Self-citationAdversary-Aware Multimodal Neural Networks for Cancer Susceptibility Prediction From Multiomics Data
Karim, M. R., Islam, T., Lange, C., Rebholz‐Schuhmann, D., & Decker, S. (2022) · IEEE Access · doi:10.1109/access.2022.3175816 · Cited by 22 · see citing works
Xiong, Y., Li, Z., Xiong, L., Song, S., Zheng, X., Tang, Z., Yuan, Z., & Li, L. (2022) · BMC Bioinformatics · doi:10.1186/s12859-022-04562-9 · Cited by 21 · see citing works
Ogunleye, A. Z., Piyawajanusorn, C., Ghislat, G., & Ballester, P. J. (2023) · Health Data Science · doi:10.34133/hds.0108 · Cited by 18 · see citing works
Self-citationPredicting Anticancer Drug Resistance Mediated by Mutations
Lin, Y., Liu, J., Chang, Y.-J., Yu, C., Yi, W., Lane, H., & Lu, C. (2022) · Pharmaceuticals · doi:10.3390/ph15020136 · Cited by 16 · see citing works
A gentle introduction to understanding preclinical data for cancer pharmaco-omic modeling
Piyawajanusorn, C., Nguyen, L. C., Ghislat, G., & Ballester, P. J. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab312 · Cited by 14 · see citing works
Self-citationBa-Alawi, W., Nair, S. K., Li, B., Mammoliti, A., Smirnov, P., Mer, A. S., Penn, L. Z., & Haibe‐Kains, B. (2022) · Cancer Research · doi:10.1158/0008-5472.can-21-2395 · Cited by 13 · see citing works
Onecha, E., Ruiz‐Heredia, Y., Martínez‐Cuadrón, D., Barragán, E., Martínez‐Sánchez, P., Linares, M., Rapado, I., de Oteyza, J. P., Magro, E., Herrera, P., et al. (2020) · British Journal of Haematology · doi:10.1111/bjh.16432 · Cited by 13 · see citing works
Zhou, L., Wang, N., Zhu, Z., Gao, H., Lu, N., Su, H., & Wang, X. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.127168 · Cited by 12 · see citing works
Shah, O. S., Chen, F., Wedn, A. M., Kashiparekh, A., Knapick, B. A., Chen, J., Savariau, L., Clifford, B., Hooda, J., Christgen, M., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.09.26.559548 · Cited by 9 · see citing works
Simeon, S., Ghislat, G., & Ballester, P. J. (2021) · Current Medicinal Chemistry · doi:10.2174/0929867328666210419134708 · Cited by 7 · see citing works
Self-citationNguyen, L. C., Naulaerts, S., Bomane, A., Bruna, A., Ghislat, G., & Ballester, P. J. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/277772 · Cited by 7 · see citing works
Self-citationAlmutiri, T., Alomar, K., & Alganmi, N. A. (2023) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2023.0140550 · Cited by 6 · see citing works
Piyawajanusorn, C., Ghislat, G., & Ballester, P. J. (2025) · npj Precision Oncology · doi:10.1038/s41698-025-00969-8 · Cited by 3 · see citing works
Self-citationCortés‐Ciriano, I., & Bender, A. (2018) · arXiv (Cornell University) · Cited by 3 · see citing works
Zhai, S., Shen, J., Hsu, J. C., & Cui, X. (2026) · Biometrical Journal · doi:10.1002/bimj.70130 · Cited by 2 · see citing works
Cheong, C. Y., Lim, C. C., Chong, Y. F., Vikneswaran, V., Salleh, A. F., & Affandi, M. (2021) · AIP conference proceedings · doi:10.1063/5.0052750 · Cited by 1 · see citing works
MGTDR: A Multi-modal Graph Transformer Network for Cancer Drug Response Prediction
Yan, C. (2024) · International Conference on Artificial Intelligence and Big Data (ICAIBD) · doi:10.1109/icaibd62003.2024.10604610 · Cited by 1 · see citing works
Rejili, M., Farahani, N., Alimohammadi, M., & Hushmandi, K. (2026) · Discover Oncology · doi:10.1007/s12672-026-05260-1 · Cited by 1 · see citing works
Guided multi-agent AI invents highly accurate, uncertainty-aware transcriptomic aging clocksPreprint
Agarwal, V., Li, O., Petty, C. A., Kassis, T., Rothemund, P. W. K., Sinclair, D. A., & Gopinath, A. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.09.08.674588 · Cited by 1 · see citing works
Computational precision therapeutics and drug repositioning
Powell, R. T. (2023) · Elsevier eBooks · doi:10.1016/b978-0-12-824010-6.00063-0
Park, S., Soh, J., & Lee, H. (2021) · Figshare · doi:10.6084/m9.figshare.14677133
Predicting Drug Resistance in Cancer Cell Lines Using Machine Learning Approaches
Tam, H. H. L., Ng, Y. M., Boolaky, M. A. A., & Ong, H. F. (2024) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm62325.2024.10822128
- Authors
- Naulaerts, S., Dang, C. C., & Ballester, P. J.
- Affiliations
- Centre National de la Recherche Scientifique (FR)
- VNU University of Engineering and Technology (VN)
- Corresponding author
- Ballester, Pedro J.
- Venue
- Oncotarget
- Publisher
- Impact Journals, LLC
- Published
- 2017-09-15
- Volume
- 8
- Issue
- 57
- Pages
- 97025-97040
- ISSN
- 1949-2553 (ISSN-L)
- PMC
- PMC5722542
- OpenAlex
- W2755779374
- Cited by
- 41
- References
- 64
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 5 · 2023: 6 · 2022: 6 · 2021: 5 · 2020: 3 · 2019: 9 · 2018: 3
- FWCI
- 3.08
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- Oncotarget (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- HAL (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- cancer drug sensitivity, genetic mutations, cancer cell lines, pharmacogenomics, precision oncology, drug resistance, predictive biomarkers, machine learning, actionable mutations, temsirolimus, 17-AAG, methotrexate
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