ArticlePloS one2015
Context Sensitive Modeling of Cancer Drug Sensitivity.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Tissue specificity of in vitro drug sensitivity.Journal of the American Medical Informatics Association : JAMIA · 2018Pooled it
- Impact of between-tissue differences on pan-cancer predictions of drug sensitivity.PLoS computational biology · 2021Article
- Computational models for predicting drug responses in cancer research.Briefings in bioinformatics · 2017Review
- Systematic identification of feature combinations for predicting drug response with Bayesian multi-view multi-task linear regression.Bioinformatics (Oxford, England) · 2017Article
- Context-specific functional module based drug efficacy prediction.BMC bioinformatics · 2016Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Recent screening of drug sensitivity in large panels of cancer cell lines provides a valuable resource towards developing algorithms that predict drug response. Since more samples provide increased statistical power, most approaches to prediction of drug sensitivity pool multiple cancer types together without distinction. However, pan-cancer results can be misleading due to the confounding effects of tissues or cancer subtypes. On the other hand, independent analysis for each cancer-type is hampered by small sample size. To balance this trade-off, we present CHER (Contextual Heterogeneity Enabled Regression), an algorithm that builds predictive models for drug sensitivity by selecting predictive genomic features and deciding which ones should-and should not-be shared across different cancers, tissues and drugs. CHER provides significantly more accurate models of drug sensitivity than comparable elastic-net-based models. Moreover, CHER provides better insight into the underlying biological processes by finding a sparse set of shared and type-specific genomic features.
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Registered trials
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