Predicting implicit associated cancer genes from OMIM and MEDLINE by a new probabilistic model.
Shanfeng ZhuYasushi OkunoGozoh TsujimotoHiroshi MamitsukaPublished in: BMC Syst. Biol. (2007)
Keyphrases
- probabilistic model
- gene selection
- dna microarray
- gene expression profiles
- cancer classification
- microarray data
- biomedical literature
- gene expression data
- cancer diagnosis
- colon cancer
- microarray
- gene expression
- candidate genes
- gene expression datasets
- gene ontology terms
- gene expression data sets
- language model
- gene expression profiling
- breast cancer
- cancer datasets
- tissue samples
- gene ontology
- biologically significant
- text mining
- gene sets
- bayesian networks
- gene regulatory networks
- ovarian cancer
- microarray datasets
- survival prediction
- cell lines
- gene expression patterns
- random forest
- related genes
- molecular biology
- gene networks
- biologically meaningful
- gene expression analysis
- protein protein interaction networks
- data sets
- lung cancer
- latent semantic indexing
- microarray data analysis
- differentially expressed genes
- cancer cells
- medline abstracts
- feature selection