TAMC: A deep-learning approach to predict motif-centric transcriptional factor binding activity based on ATAC-seq profile

• Tianqi Yang ,

  Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review & editing

  * E-mail: tianqi.yang@duke.edu (TY); ricardo.henao@duke.edu (RH)

  Affiliations Department of Pharmacology and Cancer Biology, Duke University School of Medicine, Durham, North Carolina, United States of America, Department of Cell Biology, Duke University School of Medicine, Durham, North Carolina, United States of America

  https://orcid.org/0000-0001-5358-9144

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• Ricardo Henao

  Roles Conceptualization, Project administration, Supervision, Writing – review & editing

  * E-mail: tianqi.yang@duke.edu (TY); ricardo.henao@duke.edu (RH)

  Affiliations Center for Applied Genomics and Precision Medicine, Duke University School of Medicine, Durham, North Carolina, United States of America, Department of Biostatistics and Informatics, Duke University, Durham, North Carolina, United States of America

  https://orcid.org/0000-0003-4980-845X

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