<p>A framework for DNA binding motifs prediction for nontraditional model organism transcription factors</p>

Stephanie Hao; Anthony B Garza; Yeting Li; Nofal A Ouardaoui; Cynthia A Bradham

doi:10.7490/f1000research.1119943.1

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Hao S, Garza AB, Li Y et al. A framework for DNA binding motifs prediction for nontraditional model organism transcription factors [version 1; not peer reviewed]. F1000Research 2024, 13:1214 (slides) (https://doi.org/10.7490/f1000research.1119943.1)

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A framework for DNA binding motifs prediction for nontraditional model organism transcription factors

Stephanie Hao, Anthony B Garza, Yeting Li, Nofal A Ouardaoui, Cynthia A Bradham¹

Published 11 Oct 2024 (https://doi.org/10.7490/f1000research.1119943.1)

Author Affiliations

¹ Biology, Boston University Department of Biology, Boston, Massachusetts, USA

Copyright © 2024 Hao S et al. This is an open access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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CITE

Published 11 Oct 2024

A framework for DNA binding motifs prediction for nontraditional model organism transcription factors
[version 1; not peer reviewed]

Stephanie Hao, Anthony B Garza, Yeting Li, Nofal A Ouardaoui, Cynthia A Bradham¹

Author Affiliations

¹ Biology, Boston University Department of Biology, Boston, Massachusetts, USA

Presented at

Intelligent Systems for Molecular Biology Conference (ISMB), 2025

Abstract

Competing Interests

No competing interests were disclosed

Keywords

Genomics, Gene regulatory networks, DNA binding motifs, Modular prediction algorithm, Evolutionary biology, DNA sequence analysis, Bioinformatics, Transcription factor binding sites, Computational biology, Systems biology

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