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Bayesian Markov models improve the prediction of binding motifs beyond first order
2021
NAR Genomics and Bioinformatics
Transcription factors (TFs) regulate gene expression by binding to specific DNA motifs. Accurate models for predicting binding affinities are crucial for quantitatively understanding of transcriptional regulation. Motifs are commonly described by position weight matrices, which assume that each position contributes independently to the binding energy. Models that can learn dependencies between positions, for instance, induced by DNA structure preferences, have yielded markedly improved
doi:10.1093/nargab/lqab026
pmid:33928244
pmcid:PMC8057495
fatcat:ki7o4kwzhneh7k3w7h4u2q4xem