ArticleBMC genomics2012
A Steiner tree-based method for biomarker discovery and classification in breast cancer metastasis.
Article in BMC genomics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed.
- Improving Analysis and Annotation of Microarray Data with Protein Interactions.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Comparative evaluation of network features for the prediction of breast cancer metastasis.BMC medical genomics · 2020Article
- Graph- and rule-based learning algorithms: a comprehensive review of their applications for cancer type classification and prognosis using genomic data.Briefings in bioinformatics · 2020Review
- CytoGTA: A cytoscape plugin for identifying discriminative subnetwork markers using a game theoretic approach.PloS one · 2017Article
- Network-based machine learning and graph theory algorithms for precision oncology.NPJ precision oncology · 2017Review
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- Integrative topological analysis of mass spectrometry data reveals molecular features with clinical relevance in esophageal squamous cell carcinoma.Scientific reports · 2016Article
- Toward Repurposing Metformin as a Precision Anti-Cancer Therapy Using Structural Systems Pharmacology.Scientific reports · 2016Article
- From System-Wide Differential Gene Expression to Perturbed Regulatory Factors: A Combinatorial Approach.PloS one · 2015Article
- Predicting 5-Year Survival Status of Patients with Breast Cancer based on Supervised Wavelet Method.Osong public health and research perspectives · 2014Article
- A personalized committee classification approach to improving prediction of breast cancer metastasis.Bioinformatics (Oxford, England) · 2014Article
- Using protein interaction database and support vector machines to improve gene signatures for prediction of breast cancer recurrence.Journal of medical signals and sensors · 2013Article
- Selected articles from the IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS'2011).BMC genomics · 2012Article
- Using systems biology approaches to understand cardiac inflammation and extracellular matrix remodeling in the setting of myocardial infarction.Wiley interdisciplinary reviews. Systems biology and medicineArticle
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Abstract
backgroundMetastatic breast cancer is a leading cause of cancer-related deaths in women worldwide. DNA microarray has become an important tool to help identify biomarker genes for improving the prognosis of breast cancer. Recently, it was shown that pathway-level relationships between genes can be incorporated to build more robust classification models and to obtain more useful biological insight from such models. Due to the unavailability of complete pathways, protein-protein interaction (PPI) network is becoming more popular to researcher and opens a new way to investigate the developmental process of breast cancer.
methodsIn this study, a network-based method is proposed to combine microarray gene expression profiles and PPI network for biomarker discovery for breast cancer metastasis. The key idea in our approach is to identify a small number of genes to connect differentially expressed genes into a single component in a PPI network; these intermediate genes contain important information about the pathways involved in metastasis and have a high probability of being biomarkers.
resultsWe applied this approach on two breast cancer microarray datasets, and for both cases we identified significant numbers of well-known biomarker genes for breast cancer metastasis. Those selected genes are significantly enriched with biological processes and pathways related to cancer carcinogenic process, and, importantly, have much higher stability across different datasets than in previous studies. Furthermore, our selected genes significantly increased cross-data classification accuracy of breast cancer metastasis.
conclusionsThe randomized Steiner tree based approach described in this study is a new way to discover biomarker genes for breast cancer, and improves the prediction accuracy of metastasis. Though the analysis is limited here only to breast cancer, it can be easily applied to other diseases.
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