ArticleTheScientificWorldJournal2012
Biomarker identification for prostate cancer and lymph node metastasis from microarray data and protein interaction network using gene prioritization method.
Article in TheScientificWorldJournal, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- KMT2C methyltransferase domain regulated INK4A expression suppresses prostate cancer metastasis.Molecular cancer · 2022Article
- XPNPEP2 is associated with lymph node metastasis in prostate cancer patients.Scientific reports · 2019Article
- Identification of genes associated with renal cell carcinoma using gene expression profiling analysis.Oncology letters · 2016Article
- Identifying module biomarker in type 2 diabetes mellitus by discriminative area of functional activity.BMC bioinformatics · 2015Article
- A novel method for gathering and prioritizing disease candidate genes based on construction of a set of disease-related MeSH® terms.BMC bioinformatics · 2014Article
- Article
- Screening biomarkers of prostate cancer by integrating microRNA and mRNA microarrays.Genetic testing and molecular biomarkers · 2013Article
- Article
- Feature selection and survival modeling in The Cancer Genome Atlas.International journal of nanomedicine · 2013Article
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3 authors.
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Abstract
Finding a genetic disease-related gene is not a trivial task. Therefore, computational methods are needed to present clues to the biomedical community to explore genes that are more likely to be related to a specific disease as biomarker. We present biomarker identification problem using gene prioritization method called gene prioritization from microarray data based on shortest paths, extended with structural and biological properties and edge flux using voting scheme (GP-MIDAS-VXEF). The method is based on finding relevant interactions on protein interaction networks, then scoring the genes using shortest paths and topological analysis, integrating the results using a voting scheme and a biological boosting. We applied two experiments, one is prostate primary and normal samples and the other is prostate primary tumor with and without lymph nodes metastasis. We used 137 truly prostate cancer genes as benchmark. In the first experiment, GP-MIDAS-VXEF outperforms all the other state-of-the-art methods in the benchmark by retrieving the truest related genes from the candidate set in the top 50 scores found. We applied the same technique to infer the significant biomarkers in prostate cancer with lymph nodes metastasis which is not established well.
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