bioRxivBioinformaticsPreprint
HAETAE: A highly accurate and efficient epigenome transformer for tissue-specific histone modification prediction
Seung‐Jin ParkiD, Soo-Hyun Im, Jae‐Yoon KimiD, Seon‐Young KimiD
Abstract
While genomic models trained on four bases often fail to capture cell-type specificity, we introduce HAETAE, which integrates 5-methylcytosine from long-read sequencing into a 5-base framework. By explicitly modeling epigenetic context, HAETAE achieves state-of-the-art accuracy (>0.95) with orders of magnitude fewer parameters, challenging the prevailing scaling-law paradigm. Furthermore, HAETAE deciphers tissue-specific regulatory logic, as demonstrated by revealing the distinct, context-dependent functional impact of the TERT promoter mutation across diverse tissues.
- assay: long-read sequencing
- research focus: gene regulatory network inference
- research focus: differential expression
- research focus: variant effect prediction
- approach: deep learning
- approach: transformer / foundation model
- Citations
- 0
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