Bayesian Markov models improve the prediction of binding motifs beyond first order.

Ge, Wanwan; Meier, Markus; Roth, Christian; Söding, Johannes

Ge, Wanwan; Meier, Markus; Roth, Christian; Söding, Johannes.

Affiliation

Ge W; Quantitative and Computational Biology, Max Planck Institute for Biophysical Chemistry, Am Fassberg 11, 37077 Göttingen, Germany.
Meier M; Quantitative and Computational Biology, Max Planck Institute for Biophysical Chemistry, Am Fassberg 11, 37077 Göttingen, Germany.
Roth C; Quantitative and Computational Biology, Max Planck Institute for Biophysical Chemistry, Am Fassberg 11, 37077 Göttingen, Germany.
Söding J; Quantitative and Computational Biology, Max Planck Institute for Biophysical Chemistry, Am Fassberg 11, 37077 Göttingen, Germany.

NAR Genom Bioinform ; 3(2): lqab026, 2021 Jun.

Article de En | MEDLINE | ID: mdl-33928244

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Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Type d'étude: Health_economic_evaluation / Prognostic_studies / Risk_factors_studies Langue: En Journal: NAR Genom Bioinform Année: 2021 Type de document: Article Pays d'affiliation: Allemagne Pays de publication: Royaume-Uni

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