TERfinder: A deep learning framework for multi-omics regulatory analysis in myeloid leukemia

Abstract Background Systematic identification of transcriptional and epigenetic regulators (TERs) remains a challenge in myeloid leukemia. Current methods for TER identification typically rely on single data types and show limited power for long-range regulatory interactions. Here we present TERfinder, a deep learning framework that integrates multi-omics features to predict enhancer–promoter interactions (EPIs) and characterize transcriptional regulatory programs in myeloid leukemia. Results TERfinder achieved AUC 0.9644 and AUPRC 0.9584 on held-out chromosomes, exceeding baselines without autoencoder or histone features (Table S7; DeLong test, P < 0.01). Motif enrichment identified C/EBP and ETV family TFs as candidate regulators. Single-cell regulon analysis confirmed their activity in AML progenitor populations. Single-cell analysis showed SPI1- and CEBPA-centered regulatory networks active in AML blasts, and their activity was associated with poor overall survival. A four-gene expression signature (SPI1, CEBPA, MYC, PTPN6) stratified AML patients into high- and low-risk groups (log-rank P < 0.01). Conclusions TERfinder provides a framework for multi-omics regulatory inference and candidate TF identification in myeloid leukemia.