Author

Liine Kasak

ETH Zurich

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Genomics· 1

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Assay:ATAC-seq· 1 Research Focus:benchmarking· 1 Approach:deep learning· 1 Research Focus:differential expression· 1 Research Focus:gene regulatory network inference· 1 Approach:transformer / foundation model· 1 Research Focus:variant effect prediction· 1

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Aayush GroveriD· 1 shared Ekaterina KrymovaiD· 1 shared Lin ZhangiD· 1 shared Till MuseriD· 1 shared Valentina BoevaiD· 1 shared

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Aayush GroverORCID

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Recent·Most cited

GenomicsGenome ResearchPeer-reviewedMar 1, 2026

Early feature extraction drives model performance in high-resolution chromatin accessibility prediction: A systematic evaluation of deep learning architectures

Aayush Grover, Till Muser, Liine Kasak…·Valentina Boeva

The paper evaluates deep learning architectures for high-resolution chromatin accessibility prediction, finding that early feature extraction, not downstream architecture, primarily determines performance.

DOI 10.1101/gr.281042.125

ATAC-seqdeep learning

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