7.0 years ago by
Here are some pointers on transcription factor binding prediction and Chip-seq methods. Most of them are not strictly machine learning papers, but one or the other method of ML has been employed to discover "new biology".
Predicting binding from sequence
- Weirauch, M.T. et al., 2013. Evaluation of methods for modeling transcription factor sequence specificity. Nature Biotechnology.
- Hannenhalli, S., 2008. Eukaryotic transcription factor binding sites-modeling and integrative search methods. Bioinformatics, 24(11), pp.1325–1331.
- Zambelli, F., Pesole, G. & Pavesi, G., 2009. Pscan: finding over-represented transcription factor binding site motifs in sequences from co-regulated or co-expressed genes. Nucleic Acids Research, 37(Web Server), pp.W247–W252.
- Gerstein, M.B. et al., 2012. Architecture of the human regulatory network derived from ENCODE data. Nature, 489(7414), pp.91–100.
- Wang, J. et al., 2012. Sequence features and chromatin structure around the genomic regions bound by 119 human transcription factors. Genome Research, 22(9), pp.1798–1812.
- Whitfield, T.W. et al., 2012. Functional analysis of transcription factor binding sites in human promoters. Genome Biology, 13(9), p.R50.
Abstracting tissue specificity by DNAse hypersensitivity
- Arvey, A. et al., 2012. Sequence and chromatin determinants of cell-type-specific transcription factor binding. Genome Research, 22(9), pp.1723–1734.
- Natarajan, A. et al., 2012. Predicting cell-type-specific gene expression from regions of open chromatin. Genome Research, 22(9), pp.1711–1722.
- Pique-Regi, R. et al., 2011. Accurate inference of transcription factor binding from DNA sequence and chromatin accessibility data. Genome Research, 21(3), pp.447–455.