Author

Ryan L. Keivanfar

University of California, BerkeleyORCID 0009-0007-4102-7591Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Genomics· 1

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Assay:ChIP-seq· 1 Approach:deep learning· 1 Research Focus:gene regulatory network inference· 1

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Forest YangiD· 1 shared Katherine S. PollardiD· 1 shared Nilah M. IoannidisiD· 1 shared

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2025-2025 · peak 1/yr

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1 paper

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GenomicsbioRxivPreprintApr 1, 2025

Improving interpretability of transcription factor binding models with DNA shape features

Ryan L. Keivanfar, Forest Yang, Katherine S. Pollard, Nilah M. Ioannidis

The paper introduces a model that improves transcription factor binding prediction by integrating DNA sequence and structural features, enhancing interpretability and regulatory element identification.

DOI 10.1101/2025.04.01.646034

ChIP-seqdeep learning

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