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Ensemble outlier detection and gene selection in triple-negative breast cancer data

Overview of attention for article published in BMC Bioinformatics, May 2018
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  • Above-average Attention Score compared to outputs of the same age and source (60th percentile)

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Title
Ensemble outlier detection and gene selection in triple-negative breast cancer data
Published in
BMC Bioinformatics, May 2018
DOI 10.1186/s12859-018-2149-7
Pubmed ID
Authors

Marta B. Lopes, André Veríssimo, Eunice Carrasquinha, Sandra Casimiro, Niko Beerenwinkel, Susana Vinga

Abstract

Learning accurate models from 'omics data is bringing many challenges due to their inherent high-dimensionality, e.g. the number of gene expression variables, and comparatively lower sample sizes, which leads to ill-posed inverse problems. Furthermore, the presence of outliers, either experimental errors or interesting abnormal clinical cases, may severely hamper a correct classification of patients and the identification of reliable biomarkers for a particular disease. We propose to address this problem through an ensemble classification setting based on distinct feature selection and modeling strategies, including logistic regression with elastic net regularization, Sparse Partial Least Squares - Discriminant Analysis (SPLS-DA) and Sparse Generalized PLS (SGPLS), coupled with an evaluation of the individuals' outlierness based on the Cook's distance. The consensus is achieved with the Rank Product statistics corrected for multiple testing, which gives a final list of sorted observations by their outlierness level. We applied this strategy for the classification of Triple-Negative Breast Cancer (TNBC) RNA-Seq and clinical data from the Cancer Genome Atlas (TCGA). The detected 24 outliers were identified as putative mislabeled samples, corresponding to individuals with discrepant clinical labels for the HER2 receptor, but also individuals with abnormal expression values of ER, PR and HER2, contradictory with the corresponding clinical labels, which may invalidate the initial TNBC label. Moreover, the model consensus approach leads to the selection of a set of genes that may be linked to the disease. These results are robust to a resampling approach, either by selecting a subset of patients or a subset of genes, with a significant overlap of the outlier patients identified. The proposed ensemble outlier detection approach constitutes a robust procedure to identify abnormal cases and consensus covariates, which may improve biomarker selection for precision medicine applications. The method can also be easily extended to other regression models and datasets.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 36 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 36 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 5 14%
Researcher 4 11%
Student > Ph. D. Student 4 11%
Student > Bachelor 2 6%
Lecturer 2 6%
Other 7 19%
Unknown 12 33%
Readers by discipline Count As %
Computer Science 8 22%
Biochemistry, Genetics and Molecular Biology 4 11%
Mathematics 2 6%
Immunology and Microbiology 2 6%
Agricultural and Biological Sciences 2 6%
Other 5 14%
Unknown 13 36%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 29 August 2018.
All research outputs
#12,138,968
of 15,922,891 outputs
Outputs from BMC Bioinformatics
#4,535
of 5,768 outputs
Outputs of similar age
#193,942
of 279,909 outputs
Outputs of similar age from BMC Bioinformatics
#10
of 25 outputs
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