ArticleBioinformatics (Oxford, England)2010
Inferring cancer subnetwork markers using density-constrained biclustering.
Article in Bioinformatics (Oxford, England), 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Article
- An efficient and effective method to identify significantly perturbed subnetworks in cancer.Nature computational science · 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
- EMT network-based feature selection improves prognosis prediction in lung adenocarcinoma.PloS one · 2019Article
- CytoGTA: A cytoscape plugin for identifying discriminative subnetwork markers using a game theoretic approach.PloS one · 2017Article
- Data-Driven Metabolic Pathway Compositions Enhance Cancer Survival Prediction.PLoS computational biology · 2016Article
- BicNET: Flexible module discovery in large-scale biological networks using biclustering.Algorithms for molecular biology : AMB · 2016Article
- FERAL: network-based classifier with application to breast cancer outcome prediction.Bioinformatics (Oxford, England) · 2015Article
- Discovering transnosological molecular basis of human brain diseases using biclustering analysis of integrated gene expression data.BMC medical informatics and decision making · 2015Article
- Discovery of significant pathways in breast cancer metastasis via module extraction and comparison.IET systems biology · 2014Article
- Clinical proteomic biomarkers: relevant issues on study design & technical considerations in biomarker development.Clinical and translational medicine · 2014Article
- Article
- Dissecting cancer heterogeneity with a probabilistic genotype-phenotype model.Nucleic acids research · 2013Article
- Integrative analysis of cancer-related signaling pathways.Frontiers in physiology · 2013Article
- Current composite-feature classification methods do not outperform simple single-genes classifiers in breast cancer prognosis.Frontiers in genetics · 2013Article
- Identifying stage-specific protein subnetworks for colorectal cancer.BMC proceedings · 2012Article
- Predicting disease-related subnetworks for type 1 diabetes using a new network activity score.Omics : a journal of integrative biology · 2012Article
- Network biology methods integrating biological data for translational science.Briefings in bioinformatics · 2012Review
- Sensitive detection of pathway perturbations in cancers.BMC bioinformatics · 2012Article
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7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationRecent genomic studies have confirmed that cancer is of utmost phenotypical complexity, varying greatly in terms of subtypes and evolutionary stages. When classifying cancer tissue samples, subnetwork marker approaches have proven to be superior over single gene marker approaches, most importantly in cross-platform evaluation schemes. However, prior subnetwork-based approaches do not explicitly address the great phenotypical complexity of cancer.
resultsWe explicitly address this and employ density-constrained biclustering to compute subnetwork markers, which reflect pathways being dysregulated in many, but not necessarily all samples under consideration. In breast cancer we achieve substantial improvements over all cross-platform applicable approaches when predicting TP53 mutation status in a well-established non-cross-platform setting. In colon cancer, we raise prediction accuracy in the most difficult instances from 87% to 93% for cancer versus non-cancer and from 83% to (astonishing) 92%, for with versus without liver metastasis, in well-established cross-platform evaluation schemes. AVAILABILITY: Software is available on request.
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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.