ArticleBioMed research international2013
Prediction and analysis of retinoblastoma related genes through gene ontology and KEGG.
Article in BioMed research international, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
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- A feature-based analysis identifies COL1A2 as a regulator in pancreatic cancer.Journal of enzyme inhibition and medicinal chemistry · 2019Article
- Inferring novel genes related to colorectal cancer via random walk with restart algorithm.Gene therapy · 2019Article
- The transcriptome difference between colorectal tumor and normal tissues revealed by single-cell sequencing.Journal of Cancer · 2019Article
- Downregulation of miR‑224‑5p in prostate cancer and its relevant molecular mechanism via TCGA, GEO database and in silico analyses.Oncology reports · 2018Article
- Prediction and analysis of essential genes using the enrichments of gene ontology and KEGG pathways.PloS one · 2017Article
- Genetic differences among ethnic groups.BMC genomics · 2015Article
- Discriminating between deleterious and neutral non-frameshifting indels based on protein interaction networks and hybrid properties.Molecular genetics and genomics : MGG · 2015Article
- Prediction of cancer proteins by integrating protein interaction, domain frequency, and domain interaction data using machine learning algorithms.BioMed research international · 2015Article
- Genomic Copy Number Variation Affecting Genes Involved in the Cell Cycle Pathway: Implications for Somatic Mosaicism.International journal of genomics · 2015Article
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7 authors.
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Abstract
One of the most important and challenging problems in biomedicine is how to predict the cancer related genes. Retinoblastoma (RB) is the most common primary intraocular malignancy usually occurring in childhood. Early detection of RB could reduce the morbidity and promote the probability of disease-free survival. Therefore, it is of great importance to identify RB genes. In this study, we developed a computational method to predict RB related genes based on Dagging, with the maximum relevance minimum redundancy (mRMR) method followed by incremental feature selection (IFS). 119 RB genes were compiled from two previous RB related studies, while 5,500 non-RB genes were randomly selected from Ensemble genes. Ten datasets were constructed based on all these RB and non-RB genes. Each gene was encoded with a 13,126-dimensional vector including 12,887 Gene Ontology enrichment scores and 239 KEGG enrichment scores. Finally, an optimal feature set including 1061 GO terms and 8 KEGG pathways was obtained. Analysis showed that these features were closely related to RB. It is anticipated that the method can be applied to predict the other cancer related genes as well.
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