ArticleFrontiers in genetics2013
Current composite-feature classification methods do not outperform simple single-genes classifiers in breast cancer prognosis.
Article in Frontiers in genetics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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31 citing papers in PubMed, 43 citations in OpenAlex.
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- Few-shot genes selection: subset of PAM50 genes for breast cancer subtypes classification.BMC bioinformatics · 2024Article
- Emerging treatment approaches for triple-negative breast cancer.Medical oncology (Northwood, London, England) · 2023Review
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- A mixture model to detect edges in sparse co-expression graphs with an application for comparing breast cancer subtypes.PloS one · 2021Article
- Robust edge-based biomarker discovery improves prediction of breast cancer metastasis.BMC bioinformatics · 2020Article
- Comparative evaluation of network features for the prediction of breast cancer metastasis.BMC medical genomics · 2020Article
- Significant random signatures reveals new biomarker for breast cancer.BMC medical genomics · 2019Article
- Predictive modelling using pathway scores: robustness and significance of pathway collections.BMC bioinformatics · 2019Article
- A data-driven interactome of synergistic genes improves network-based cancer outcome prediction.PLoS computational biology · 2019Article
- A Comprehensive Survey of Tools and Software for Active Subnetwork Identification.Frontiers in genetics · 2019Review
- EMT network-based feature selection improves prognosis prediction in lung adenocarcinoma.PloS one · 2019Article
- McImpute: Matrix Completion Based Imputation for Single Cell RNA-seq Data.Frontiers in genetics · 2019Article
- De novo pathway-based biomarker identification.Nucleic acids research · 2017Article
- Identification of breast cancer prognostic modules based on weighted protein-protein interaction networks.Oncology letters · 2017Article
- Network-based analysis of omics data: the LEAN method.Bioinformatics (Oxford, England) · 2017Article
- Data-Driven Metabolic Pathway Compositions Enhance Cancer Survival Prediction.PLoS computational biology · 2016Article
- Gene expression prediction using low-rank matrix completion.BMC bioinformatics · 2016Article
- Taking Bioinformatics to Systems Medicine.Methods in molecular biology (Clifton, N.J.) · 2016Review
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5 authors at 4 institutions in 2 countries.
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No grant is acknowledged in the PubMed record.
Abstract
Integrating gene expression data with secondary data such as pathway or protein-protein interaction data has been proposed as a promising approach for improved outcome prediction of cancer patients. Methods employing this approach usually aggregate the expression of genes into new composite features, while the secondary data guide this aggregation. Previous studies were limited to few data sets with a small number of patients. Moreover, each study used different data and evaluation procedures. This makes it difficult to objectively assess the gain in classification performance. Here we introduce the Amsterdam Classification Evaluation Suite (ACES). ACES is a Python package to objectively evaluate classification and feature-selection methods and contains methods for pooling and normalizing Affymetrix microarrays from different studies. It is simple to use and therefore facilitates the comparison of new approaches to best-in-class approaches. In addition to the methods described in our earlier study (Staiger et al., 2012), we have included two prominent prognostic gene signatures specific for breast cancer outcome, one more composite feature selection method and two network-based gene ranking methods. Employing the evaluation pipeline we show that current composite-feature classification methods do not outperform simple single-genes classifiers in predicting outcome in breast cancer. Furthermore, we find that also the stability of features across different data sets is not higher for composite features. Most stunningly, we observe that prediction performances are not affected when extracting features from randomized PPI networks.
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