Predicting gene expression in T cell differentiation from histone modifications and transcription factor binding affinities by linear mixture models.
Ivan G. CostaHelge G. RoiderThaís Gaudencio do RêgoFrancisco de A. T. de CarvalhoPublished in: BMC Bioinform. (2011)
Keyphrases
- mixture model
- dna binding
- gene expression
- transcription factors
- binding sites
- cell cycle
- microarray
- gene regulation
- genome wide
- probabilistic model
- transcriptional regulation
- transcription factor binding sites
- regulatory networks
- biological processes
- generative model
- em algorithm
- expectation maximization
- gene expression data
- language model
- maximum likelihood
- model selection
- unsupervised learning
- dna sequences
- microarray data
- high throughput
- signaling pathways
- sequence data
- computational biology
- component analysis
- pairwise
- motif discovery
- feature space
- highly correlated
- data acquisition
- protein interaction
- co occurrence
- knowledge discovery
- experimental conditions
- saccharomyces cerevisiae
- feature extraction
- regulatory elements
- bayesian networks