RIP: the regulatory interaction predictor - a machine learning-based approach for predicting target genes of transcription factors.
Tobias BauerRoland EilsRainer KönigPublished in: Bioinform. (2011)
Keyphrases
- transcription factors
- gene expression
- machine learning
- computational biology
- differentially expressed
- dna binding
- gene regulation
- binding sites
- gene regulatory
- microarray
- regulatory elements
- saccharomyces cerevisiae
- biological processes
- regulatory networks
- differentially expressed genes
- transcriptional regulation
- microarray data
- transcription factor binding sites
- dna sequences
- protein interaction
- cell cycle
- genome wide
- systems biology
- gene expression data
- sequence data
- cis regulatory
- biomedical literature
- component analysis
- gene sets
- high throughput
- transcriptional regulatory
- gene selection
- gene regulatory networks
- molecular biology
- machine learning methods
- information extraction
- text mining
- microarray data analysis
- natural language processing
- gene expression profiles
- highly correlated
- protein sequences
- post transcriptional
- knowledge discovery
- motif discovery
- high dimensionality
- gene function
- data mining
- co occurrence
- interaction networks
- signaling pathways
- microarray datasets
- biological networks
- data analysis
- experimental conditions