ArticleBMC bioinformatics2014
A p-Median approach for predicting drug response in tumour cells.
Article in BMC bioinformatics, 2014. 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.
- Drug repurposing-an emerging strategy in cancer therapeutics.Naunyn-Schmiedeberg's archives of pharmacology · 2022Review
- Improving drug response prediction by integrating multiple data sources: matrix factorization, kernel and network-based approaches.Briefings in bioinformatics · 2021Article
- Anticancer Drug Response Prediction in Cell Lines Using Weighted Graph Regularized Matrix Factorization.Molecular therapy. Nucleic acids · 2019Article
- Comprehensive anticancer drug response prediction based on a simple cell line-drug complex network model.BMC bioinformatics · 2019Article
- Computational models for predicting drug responses in cancer research.Briefings in bioinformatics · 2017Review
Corrections and comments
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Authors and funding
3 authors.
Funding
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Abstract
backgroundThe complexity of biological data related to the genetic origins of tumour cells, originates significant challenges to glean valuable knowledge that can be used to predict therapeutic responses. In order to discover a link between gene expression profiles and drug responses, a computational framework based on Consensus p-Median clustering is proposed. The main goal is to simultaneously predict (in silico) anticancer responses by extracting common patterns among tumour cell lines, selecting genes that could potentially explain the therapy outcome and finally learning a probabilistic model able to predict the therapeutic responses.
resultsThe experimental investigation performed on the NCI60 dataset highlights three main findings: (1) Consensus p-Median is able to create groups of cell lines that are highly correlated both in terms of gene expression and drug response; (2) from a biological point of view, the proposed approach enables the selection of genes that are strongly involved in several cancer processes; (3) the final prediction of drug responses, built upon Consensus p-Median and the selected genes, represents a promising step for predicting potential useful drugs.
conclusionThe proposed learning framework represents a promising approach predicting drug response in tumour cells.
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