ArticleBioinformatics (Oxford, England)2015
FERAL: network-based classifier with application to breast cancer outcome prediction.
Article in Bioinformatics (Oxford, England), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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20 citing papers in PubMed, 39 citations in OpenAlex.
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- DIABLO: an integrative approach for identifying key molecular drivers from multi-omics assays.Bioinformatics (Oxford, England) · 2019Article
- A data-driven interactome of synergistic genes improves network-based cancer outcome prediction.PLoS computational biology · 2019Article
- Feature Selection for Longitudinal Data by Using Sign Averages to Summarize Gene Expression Values over Time.BioMed research international · 2019Article
- EMT network-based feature selection improves prognosis prediction in lung adenocarcinoma.PloS one · 2019Article
- Personalized Integrated Network Modeling of the Cancer Proteome Atlas.Scientific reports · 2018Article
- Association between expression of random gene sets and survival is evident in multiple cancer types and may be explained by sub-classification.PLoS computational biology · 2018Article
- De novo pathway-based biomarker identification.Nucleic acids research · 2017Article
- Identification of associations between genotypes and longitudinal phenotypes via temporally-constrained group sparse canonical correlation analysis.Bioinformatics (Oxford, England) · 2017Article
- Label-Informed Non-negative Matrix Factorization with Manifold Regularization for Discriminative Subnetwork Detection.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2016Article
- Data-Driven Metabolic Pathway Compositions Enhance Cancer Survival Prediction.PLoS computational biology · 2016Article
- Data Requirements for Model-Based Cancer Prognosis Prediction.Cancer informatics · 2015Article
- A Pipeline for Integrated Theory and Data-Driven Modeling of Biomedical Data.IEEE/ACM transactions on computational biology and bioinformaticsArticle
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationBreast cancer outcome prediction based on gene expression profiles is an important strategy for personalize patient care. To improve performance and consistency of discovered markers of the initial molecular classifiers, network-based outcome prediction methods (NOPs) have been proposed. In spite of the initial claims, recent studies revealed that neither performance nor consistency can be improved using these methods. NOPs typically rely on the construction of meta-genes by averaging the expression of several genes connected in a network that encodes protein interactions or pathway information. In this article, we expose several fundamental issues in NOPs that impede on the prediction power, consistency of discovered markers and obscures biological interpretation.
resultsTo overcome these issues, we propose FERAL, a network-based classifier that hinges upon the Sparse Group Lasso which performs simultaneous selection of marker genes and training of the prediction model. An important feature of FERAL, and a significant departure from existing NOPs, is that it uses multiple operators to summarize genes into meta-genes. This gives the classifier the opportunity to select the most relevant meta-gene for each gene set. Extensive evaluation revealed that the discovered markers are markedly more stable across independent datasets. Moreover, interpretation of the marker genes detected by FERAL reveals valuable mechanistic insight into the etiology of breast cancer. AVAILABILITY AND IMPLEMENTATION: All code is available for download at: http://homepage.tudelft.nl/53a60/resources/FERAL/FERAL.zip.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.