A DSSM network for inferring and prioritizing cell-type-specific regulons using single-cell RNA-seq data.

ABSTRACT:

Background

Transcription factors and their target genes form regulatory modules known as regulons, which exhibit significant specificity across various cell types. The integration of single-cell transcriptome data, transcription factor motif data, and ChIP-seq data presents a challenging task in identifying cell-type-specific regulons and examining their activities.

Results

In response, this study presents a Deep Structured Semantic Model for inferring and prioritizing cell-type-specific Regulons (DSSMReg). This approach utilizes single-cell transcriptome and transcription factor motif data to map transcription factors and target genes into a low-dimensional semantic space, resulting in the generation of feature vectors. The model then computes the cosine similarity between

SUBMITTER: Fan Y 

PROVIDER: S-EPMC12798040 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature