CTF-BIND
(Causal Transcription Factor- Bayesian Integration of Network Dynamics)
(Causal Transcription Factor- Bayesian Integration of Network Dynamics)
Steps in Bayesian network reconstruction. The process involves: 1) Estimation of Directed Acyclic Graphs (DAGs) between target genes (TGs), transcription factors (TFs), and their protein-protein interaction (PPI) partners using expression data and prior knowledge. 2) Identification of significant DAGs based on convergence criteria. 3) Modeling of significant DAGs with a generalized linear model assuming a multivariate Gaussian distribution. 4) Parameter estimation between TGs, TFs, and PPI partners using sparse regularization. 5) Selection of the optimal regularization penalty based on different rho lambda values. 6) Final parameter estimation using the optimal penalty and criteria-based parameter selection.
Overview of the architecture and workflow of the CTFBind framework for predicting transcription factor (TF) binding activity. Input data includes TF causal networks, gene expression profiles, ChIP-seq data, TF 3D structures, and promoter sequences. A Graph-Transformer processes network features, while a Transformer encodes DNA sequence information.
This figure illustrates how CTFBind enables accurate TF binding predictions directly from transcriptome data, eliminating the need for additional ChIP-seq experiments. Features are concatenated, pooled, and passed through an XGBoost classifier to predict TF binding probabilities for target genes (TGs) under specific conditions.