The DOI matches its Crossref record; 50 of the 50 references it cites were found in a registry or an index.
A community effort to assess and improve drug sensitivity prediction algorithms
Open accessAbstract
Predicting the best treatment strategy from genomic information is a core goal of precision medicine. Here we focus on predicting drug response based on a cohort of genomic, epigenomic and proteomic profiling data sets measured in human breast cancer cell lines. Through a collaborative effort between the National Cancer Institute (NCI) and the Dialogue on Reverse Engineering Assessment and Methods (DREAM) project, we analyzed a total of 44 drug sensitivity prediction algorithms. The top-performing approaches modeled nonlinear relationships and incorporated biological pathway information. We found that gene expression microarrays consistently provided the best predictive power of the individual profiling data sets; however, performance was increased by including multiple, independent data sets. We discuss the innovations underlying the top-performing methodology, Bayesian multitask MKL, and we provide detailed descriptions of all methods. This study establishes benchmarks for drug sensitivity prediction and identifies approaches that can be leveraged for the development of new methods.
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Cites
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Applications of machine learning in drug discovery and development
Vamathevan, J., Clark, D., Czodrowski, P., Dunham, I., Ferran, E., Lee, G., Li, B., Madabhushi, A., Shah, P., Spitzer, M., et al. (2019) · Nature Reviews Drug Discovery · doi:10.1038/s41573-019-0024-5 · Cited by 3,228
A Landscape of Pharmacogenomic Interactions in Cancer
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Self-citationArtificial Intelligence Transforms the Future of Health Care
Noorbakhsh-Sabet, N., Zand, R., Zhang, Y., & Abedi, V. (2019) · The American Journal of Medicine · doi:10.1016/j.amjmed.2019.01.017 · Cited by 626
Predicting drug response and synergy using a deep learning model of human cancer cells
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Artificial intelligence-based multi-omics analysis fuels cancer precision medicine
He, X., Liu, X., Zuo, F., Shi, H., & Jing, J. (2023) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2022.12.009 · Cited by 445
Metadata
- Authors
- 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., Khan, S. A., Mpindi, J.-P., Kallioniemi, O., Honkela, A., Aittokallio, T., Wennerberg, K., Collins, J. J., Gallahan, D., Singer, D., Saez-Rodriguez, J., Kaski, S., Gray, J. W., & Stolovitzky, G.
- Venue
- Nature Biotechnology
- Published
- 2014-06-01
- Volume
- 32
- Issue
- 12
- Pages
- 1202-1212
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- OpenAlex
- W2108933868
- Cited by
- 772
- References
- 50
- FWCI
- 58.61
- Type
- journal article
- Language
- en
- Open access
- bronze
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- The paper
- 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., Khan, S. A., Mpindi, J.-P., Kallioniemi, O., Honkela, A., Aittokallio, T., Wennerberg, K., Collins, J. J., Gallahan, D., Singer, D., . . . Stolovitzky, G. (2014). A community effort to assess and improve drug sensitivity prediction algorithms. Nature Biotechnology. https://doi.org/10.1038/nbt.2877
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
