Please use this identifier to cite or link to this item:
http://hdl.handle.net/2445/189850
Title: | DNAffinity: a machine-learning approach to predict DNA binding affinities of transcription factors |
Author: | Barissi, Sandro Sala, Alba Wieczór, Miłosz Battistini, Federica Orozco López, Modesto |
Keywords: | ADN Genòmica Biologia computacional DNA Genomics Computational biology |
Issue Date: | 26-Aug-2022 |
Publisher: | Oxford University Press |
Abstract: | We present a physics-based machine learning approach to predict in vitro transcription factor binding affinities from structural and mechanical DNA properties directly derived from atomistic molecular dynamics simulations. The method is able to predict affinities obtained with techniques as different as uPBM, gcPBM and HT-SELEX with an excellent performance, much better than existing algorithms. Due to its nature, the method can be extended to epigenetic variants, mismatches, mutations, or any non-coding nucleobases. When complemented with chromatin structure information, our in vitro trained method provides also good estimates of in vivo binding sites in yeast. |
Note: | Reproducció del document publicat a: https://doi.org/10.1093/nar/gkac708 |
It is part of: | Nucleic Acids Research, 2022, vol. 50, num. 16, p. 9105-9114 |
URI: | http://hdl.handle.net/2445/189850 |
Related resource: | https://doi.org/10.1093/nar/gkac708 |
ISSN: | 0305-1048 |
Appears in Collections: | Articles publicats en revistes (Bioquímica i Biomedicina Molecular) |
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