Department of Biological Sciences
Despite the diversity of motif representations and search algorithms, the de novo computational identification of transcription factor binding sites remains constrained by the limited accuracy of existing algorithms and the need for user-specified input parameters that describe the motif being sought.ResultsWe present a novel ensemble learning method, SCOPE, that is based on the assumption that transcription factor binding sites belong to one of three broad classes of motifs: non-degenerate, degenerate and gapped motifs. SCOPE employs a unified scoring metric to combine the results from three motif finding algorithms each aimed at the discovery of one of these classes of motifs. We found that SCOPE's performance on 78 experimentally characterized regulons from four species was a substantial and statistically significant improvement over that of its component algorithms. SCOPE outperformed a broad range of existing motif discovery algorithms on the same dataset by a statistically significant margin.
Chakravarty A, Carlson JM, Khetani RS, Gross RH. A novel ensemble learning method for de novo computational identification of DNA binding sites. BMC Bioinformatics. 2007 Jul 12;8:249. doi: 10.1186/1471-2105-8-249. PMID: 17626633; PMCID: PMC1950314.
Dartmouth Digital Commons Citation
Chakravarty, Arijit; Carlson, Jonathan M.; Khetani, Radhika S.; and Gross, Robert H H., "A Novel Ensemble Learning Method for de Novo Computational Identification of DNA Binding Sites" (2007). Dartmouth Scholarship. 570.