ArticleNucleic acids research2017
De novo pathway-based biomarker identification.
Article in Nucleic acids research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- A unified mediation analysis framework for integrative cancer proteogenomics with clinical outcomes.Bioinformatics (Oxford, England) · 2023Article
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- Artificial Intelligence in Bulk and Single-Cell RNA-Sequencing Data to Foster Precision Oncology.International journal of molecular sciences · 2021Review
- A Network-Based Methodology to Identify Subnetwork Markers for Diagnosis and Prognosis of Colorectal Cancer.Frontiers in genetics · 2021Article
- Molecular Subclassification Based on Crosstalk Analysis Improves Prediction of Prognosis in Colorectal Cancer.Frontiers in genetics · 2021Article
- NOX5-induced uncoupling of endothelial NO synthase is a causal mechanism and theragnostic target of an age-related hypertension endotype.PLoS biology · 2020Article
- 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
- Edgetic perturbation signatures represent known and novel cancer biomarkers.Scientific reports · 2020Article
- CAncer bioMarker Prediction Pipeline (CAMPP)-A standardized framework for the analysis of quantitative biological data.PLoS computational biology · 2020Article
- Comparison of pathway and gene-level models for cancer prognosis prediction.BMC bioinformatics · 2020Article
- Dynamically characterizing individual clinical change by the steady state of disease-associated pathway.BMC bioinformatics · 2019Article
- Predictive modelling using pathway scores: robustness and significance of pathway collections.BMC bioinformatics · 2019Article
- Twiner: correlation-based regularization for identifying common cancer gene signatures.BMC bioinformatics · 2019Article
- Article
- From single drug targets to synergistic network pharmacology in ischemic stroke.Proceedings of the National Academy of Sciences of the United States of America · 2019Article
- A data-driven interactome of synergistic genes improves network-based cancer outcome prediction.PLoS computational biology · 2019Article
- Time-Resolved Systems Medicine Reveals Viral Infection-Modulating Host Targets.Systems medicine (New Rochelle, N.Y.) · 2019Article
- Personalized Integrated Network Modeling of the Cancer Proteome Atlas.Scientific reports · 2018Article
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6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Gene expression profiles have been extensively discussed as an aid to guide the therapy by predicting disease outcome for the patients suffering from complex diseases, such as cancer. However, prediction models built upon single-gene (SG) features show poor stability and performance on independent datasets. Attempts to mitigate these drawbacks have led to the development of network-based approaches that integrate pathway information to produce meta-gene (MG) features. Also, MG approaches have only dealt with the two-class problem of good versus poor outcome prediction. Stratifying patients based on their molecular subtypes can provide a detailed view of the disease and lead to more personalized therapies. We propose and discuss a novel MG approach based on de novo pathways, which for the first time have been used as features in a multi-class setting to predict cancer subtypes. Comprehensive evaluation in a large cohort of breast cancer samples from The Cancer Genome Atlas (TCGA) revealed that MGs are considerably more stable than SG models, while also providing valuable insight into the cancer hallmarks that drive them. In addition, when tested on an independent benchmark non-TCGA dataset, MG features consistently outperformed SG models. We provide an easy-to-use web service at http://pathclass.compbio.sdu.dk where users can upload their own gene expression datasets from breast cancer studies and obtain the subtype predictions from all the classifiers.
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