Author

Forest Yang

National Institutes of HealthORCID 0009-0004-1924-6067Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Assay:bulk RNA-seq· 1 Research Focus:cell type annotation· 1 Assay:ChIP-seq· 1 Approach:deep learning· 1 Research Focus:differential expression· 1 Research Focus:gene regulatory network inference· 1 Organism:human· 1 Approach:statistical model· 1

Frequent Co-Authors

Barbara S. MalloniD· 1 shared Dragan MariciD· 1 shared Katherine S. PollardiD· 1 shared Kevin G. CheniD· 1 shared Kory R. JohnsoniD· 1 shared Kyeyoon Park· 1 shared Nilah M. IoannidisiD· 1 shared Pamela Gehron RobeyiD· 1 shared

Activity by Year

2025-2025 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 24 mutual co-author links

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1 shared paperCell Biology

Barbara S. MallonORCID

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2 papers

Recent·Most cited

Cell BiologyStem CellsPeer-reviewedAug 22, 2025

Resistance to Naive and Formative Pluripotency Conversion in RSeT Human Embryonic Stem Cells

Kevin G. Chen, Kory R. Johnson, Kyeyoon Park…·Pamela Gehron Robey

This study characterizes a new pluripotent state in RSeT hESCs, showing resistance to naive and formative conversion and distinct transcriptomic and signaling features.

4 citations

humanbulk RNA-seq

𝕏 PostBluesky

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

𝕏 PostBluesky