CFA

CFA annotates transcriptional roles of cis-regulatory modules (CRMs) in Metazoa by applying an explainable deep learning model based on epigenetic code patterns.

Key Features:

• CRM Functional Classification: Predicts promoter, enhancer, and insulator roles of cis-regulatory modules using epigenetic signal patterns.
• Epigenetic Code Integration: Integrates combinations of epigenetic marks to improve accuracy in CRM transcriptional role annotation.
• Explainable Deep Learning Model: Uses interpretable deep learning approaches to identify epigenetic code patterns associated with CRM functions.
• Reduced False-Positive Annotation: Recognizes higher-order epigenetic code combinations to improve specificity in functional classification.

Scientific Applications:

• Gene Regulation Analysis: Annotates cis-regulatory modules to study promoter, enhancer, and insulator roles in transcriptional regulation.
• Epigenomic Data Interpretation: Analyzes epigenetic signals to infer regulatory element functions in metazoan genomes.

Methodology:

CFA applies an explainable deep learning model that analyzes epigenetic code patterns to classify cis-regulatory modules as promoters, enhancers, or insulators.