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@INPROCEEDINGS{Piroozeh:1052350,
author = {Piroozeh, Zohreh and Akerman, Ildem and Kesselheim, Stefan
and Kalinina, Olga and Bazarova, Alina},
title = {{I}nterpretable prediction of {DNA} replication origins in
{S}. cerevisiae using attention-based motif discovery},
reportid = {FZJ-2026-00952},
year = {2025},
abstract = {In a living cell, DNA replication begins at multiple
genomic sites, called replicationorigins. Identifying these
origins and their underlying base sequence compositionis
crucial for understanding replication process. Existing
machine learningmethods for origin prediction often require
labor-intensive feature engineering orlack interpretability.
Here, we employ DNABERT to predict yeast replication
originsand uncover sequence motifs by combining attention
maps with MEME, aclassical bioinformatics tool. Our approach
eliminates manual feature extractionand identifies
biologically relevant motifs across datasets of varying
complexity.This work advances interpretable machine learning
in genomics, offering a potentiallygeneralizable framework
for origin prediction and motif discovery.},
month = {Apr},
date = {2025-04-24},
organization = {ICLR 2025, Singapore (Singapore), 24
Apr 2025 - 28 Apr 2025},
subtyp = {Other},
cin = {JSC},
cid = {I:(DE-Juel1)JSC-20090406},
pnm = {5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs)
and Research Groups (POF4-511) / Helmholtz AI Consultant
Team FB Information (E54.303.11)},
pid = {G:(DE-HGF)POF4-5112 / G:(DE-Juel-1)E54.303.11},
typ = {PUB:(DE-HGF)24},
doi = {10.34734/FZJ-2026-00952},
url = {https://juser.fz-juelich.de/record/1052350},
}