MEDUSA (Motif Element Detection Using Sequence Agglomeration) is an integrative method for learning motif models of transcription factor binding sites by incorporating promoter sequence and gene expression data. We use a modern large-margin machine learning approach, based on boosting, to enable feature selection from the high-dimensional search space of candidate binding sequences while avoiding overfitting.
- Linux/Windows /MacOsX
:: MORE INFORMATION
Learning regulatory programs that accurately predict differential expression with MEDUSA.
Kundaje A, Lianoglou S, Li X, Quigley D, Arias M, Wiggins CH, Zhang L, Leslie C.
Ann N Y Acad Sci. 2007 Dec;1115:178-202. Epub 2007 Oct 12.