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A p-Median approach for predicting drug response in tumour cells.

Fersini E, Messina E, Archetti F - BMC Bioinformatics (2014)

Bottom Line: The 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.The 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.The proposed learning framework represents a promising approach predicting drug response in tumour cells.

View Article: PubMed Central - PubMed

ABSTRACT

Background: The 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.

Results: The 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.

Conclusion: The proposed learning framework represents a promising approach predicting drug response in tumour cells.

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Related in: MedlinePlus

Correlation indices for the Sherf dataset. The y and x-coordinates denote the average Pearson correlation in the drug and gene space respectively. The correlation indices for all the reported series have been averaged over the leave-one-out cross validation folds. Each point of the series for Consensus p-Median corresponds to a solution obtained according to the parameter μ, while the series for STVQ reports values for α={0,0.1,0.2,⋯,1.0}.
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Fig3: Correlation indices for the Sherf dataset. The y and x-coordinates denote the average Pearson correlation in the drug and gene space respectively. The correlation indices for all the reported series have been averaged over the leave-one-out cross validation folds. Each point of the series for Consensus p-Median corresponds to a solution obtained according to the parameter μ, while the series for STVQ reports values for α={0,0.1,0.2,⋯,1.0}.

Mentions: In Figures 3 and 4, a comparison in terms of correlation (averaged on the leave-one-out folds) between the investigated clustering approaches is depicted reporting the traditional p-Median, Probabilistic D-Clustering, k-Means, SVTQ, Relational k-Means and the proposed Consensus p-Median. For the Consensus p-Median two series are reported, i.e. Consensus p-Median (g-d) and Consensus p-Median (d-g).Figure 3


A p-Median approach for predicting drug response in tumour cells.

Fersini E, Messina E, Archetti F - BMC Bioinformatics (2014)

Correlation indices for the Sherf dataset. The y and x-coordinates denote the average Pearson correlation in the drug and gene space respectively. The correlation indices for all the reported series have been averaged over the leave-one-out cross validation folds. Each point of the series for Consensus p-Median corresponds to a solution obtained according to the parameter μ, while the series for STVQ reports values for α={0,0.1,0.2,⋯,1.0}.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4222443&req=5

Fig3: Correlation indices for the Sherf dataset. The y and x-coordinates denote the average Pearson correlation in the drug and gene space respectively. The correlation indices for all the reported series have been averaged over the leave-one-out cross validation folds. Each point of the series for Consensus p-Median corresponds to a solution obtained according to the parameter μ, while the series for STVQ reports values for α={0,0.1,0.2,⋯,1.0}.
Mentions: In Figures 3 and 4, a comparison in terms of correlation (averaged on the leave-one-out folds) between the investigated clustering approaches is depicted reporting the traditional p-Median, Probabilistic D-Clustering, k-Means, SVTQ, Relational k-Means and the proposed Consensus p-Median. For the Consensus p-Median two series are reported, i.e. Consensus p-Median (g-d) and Consensus p-Median (d-g).Figure 3

Bottom Line: The 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.The 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.The proposed learning framework represents a promising approach predicting drug response in tumour cells.

View Article: PubMed Central - PubMed

ABSTRACT

Background: The 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.

Results: The 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.

Conclusion: The proposed learning framework represents a promising approach predicting drug response in tumour cells.

Show MeSH
Related in: MedlinePlus