Authors
Risa Okubo, Takashi Morikura, Yusuke Hiki, Yuta Tokuoka, Tetsuya J Kobayashi, Takahiro G Yamada, Akira Funahashi
Published in
NAR genomics and bioinformatics. Volume 8. Issue 1. Pages lqag022. Epub Feb 25, 2026.
Abstract
Identifying regulatory relationships between transcription factors (TFs) and genes is essential to understand diverse biological phenomena related to gene expression. Recently, deep learning-based models to predict TFs that bind to genes from nucleotide sequences of the target genes have been developed, yet these models are trained to predict known TFs only. Here, we developed a deep learning model, GReNIMJA (Gene Regulatory Network Inference by Mixing and Jointing features of Amino acid and nucleotide sequences), to predict gene regulation even by unknown TFs. Our model is designed to mix the features of the TF amino acid sequences and nucleotide sequences of the target genes using a 2D Long Short-Term Memory architecture and to perform binary classification with the aim of determining the presence or absence of a regulatory relationship. By explicitly modeling interactions between TFs and genes, our model can predict gene regulation for unknown TFs. The accuracy of our model in predicting regulatory relationships was 84.4% for known TFs (higher than those of conventional models) and 68.5% for unknown TFs; the latter is an unsolved task for conventional deep learning-based models. We expect our model to advance identification of unknown gene regulatory networks and contribute to the understanding of diverse biological phenomena.
PMID:
41782896
Bibliographic data and abstract were imported from PubMed on 17 Sep 2026.
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