Decoding yeast transcriptional regulation via a data-and mechanism-driven distributed large-scale network model.

ABSTRACT: The complex transcriptional regulatory relationships among genes influence gene expression levels and play a crucial role in determining cellular phenotypes. In this study, we propose a novel, distributed, large-scale transcriptional regulatory neural network model (DLTRNM), which integrates prior knowledge into the reconstruction of pre-trained machine learning models, followed by fine-tuning. Using Saccharomyces cerevisiae as a case study, the curated transcriptional regulatory relationships are used to define the interactions between transcription factors (TFs) and their target genes (TGs). Subsequently, DLTRNM is pre-trained on pan-transcriptomic data and fine-tuned with time-series data, enabling it to accurately predict regulatory correlations. Additionally, DLTRNM can help id

SUBMITTER: Fan X 

PROVIDER: S-EPMC12268561 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature