SynthesisThe pharmacogenomics journal2012
Background gene expression networks significantly enhance drug response prediction by transcriptional profiling.
Synthesis in The pharmacogenomics journal, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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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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Who cites it
5 citing papers in PubMed.
- Drug response prediction using graph representation learning and Laplacian feature selection.BMC bioinformatics · 2022Article
- Integrated Genomic Medicine: A Paradigm for Rare Diseases and Beyond.Advances in genetics · 2017Review
- Concept of pharmacogenomics and future considerations.CNS neuroscience & therapeutics · 2013Review
- A multi-site feasibility study for personalized medicine in canines with osteosarcoma.Journal of translational medicine · 2013Article
- Machine learning prediction of cancer cell sensitivity to drugs based on genomic and chemical properties.PloS one · 2013Article
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Authors and funding
2 authors.
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Abstract
A central goal of gene expression studies coupled with drug response screens is to identify predictive profiles that can be exploited to stratify patients. Numerous methods have been proposed towards this end, most of them focusing on novel statistical methods and model selection techniques that attempt to uncover groups of genes, whose expression profiles are directly and robustly correlated with drug response. However, biological systems process information through the crosstalk of multiple signaling networks, whose ultimate phenotypic consequences may only be determined by the combined input of relevant interacting systems. By restricting predictive signatures to direct gene-drug correlations, biologically meaningful interactions that may serve as superior predictors are ignored. Here we demonstrate that predictive signatures, which incorporate the interaction between background gene expression patterns and individual predictive probes, can provide superior models than those that directly relate gene expression levels to pharmacological response, and thus should be more widely utilized in pharmacogenetic studies.
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