ArticlePloS one2015
A network flow-based method to predict anticancer 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.
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Who cites it
5 citing papers in PubMed.
- High throughput estimation of functional cell activities reveals disease mechanisms and predicts relevant clinical outcomes.Oncotarget · 2017Article
- Neuroblastoma, a Paradigm for Big Data Science in Pediatric Oncology.International journal of molecular sciences · 2016Review
- A network-based phenotype mapping approach to identify genes that modulate drug response phenotypes.Scientific reports · 2016Article
- Integrating Domain Specific Knowledge and Network Analysis to Predict Drug Sensitivity of Cancer Cell Lines.PloS one · 2016Article
- Using activation status of signaling pathways as mechanism-based biomarkers to predict drug sensitivity.Scientific reports · 2015Article
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Authors and funding
4 authors.
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Abstract
Predicting anticancer drug sensitivity can enhance the ability to individualize patient treatment, thus making development of cancer therapies more effective and safe. In this paper, we present a new network flow-based method, which utilizes the topological structure of pathways, for predicting anticancer drug sensitivities. Mutations and copy number alterations of cancer-related genes are assumed to change the pathway activity, and pathway activity difference before and after drug treatment is used as a measure of drug response. In our model, Contributions from different genetic alterations are considered as free parameters, which are optimized by the drug response data from the Cancer Genome Project (CGP). 10-fold cross validation on CGP data set showed that our model achieved comparable prediction results with existing elastic net model using much less input features.
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