#16 · Systems biology
Interaction networks give molecular features causal and topological meaning
The common thread is placing molecular measurements inside an explicit relational structure so that individual features gain functional or topological meaning. Causal discovery learns directed gene networks from joint radiation perturbation and RNA-seq data in human cells. BayesTx integrates TCGA patient and DepMap cell line data through Bayesian networks to rank 47 transcription factors by causal cancer impact. ChEA-KG builds a signed, directed TF regulatory network from ChIP-seq and RNA-seq data for subnetwork enrichment, and ProteinNetworkSight converts feature vectors into interactive PPI networks. MOLA applies topological data analysis to multiomic loops, linking them to sample traits and function. What appears newer is the focus on sample-level structure, as MOLA links loops to sample traits, alongside explicit directed or causal structure over association alone.
Published papers
ProteinNetworkSight: a user-friendly platform for transforming co-expression patterns into actionable therapeutic insights through interactive network visualization.
ProteinNetworkSight is a web server that converts multiple feature vectors from ML or statistical decomposition into interactive PPI networks in one run,…
ChEA-KG and ChEA-KG-TS: a network-based transcription factor enrichment analysis tool with an accompanying time-series workflow.
ChEA-KG builds a signed, directed TF regulatory network from ChIP-seq and RNA-seq data for subnetwork enrichment, plus ChEA-KG-TS for time-series TF module…
Preprints
Causal Discovery of Radiation Response Mechanisms in Human Cells
Applies causal discovery to jointly model radiation perturbations and RNA-seq expression, learning directed gene networks of human cell radiation responses…
MOLA: a novel topological data analysis framework for analyzing multiomic loops in precision medicine
MOLA is a topological data analysis framework for detecting and filtering multiomic loops, linking them to sample traits and function, tested on influenza…
A Bayesian Network-Based Framework for Causal Cancer Drug Target Discovery Integrating Patient and Cell Line Data
BayesTx merges TCGA patient and DepMap cell line data via Bayesian networks to rank 47 transcription factors by causal cancer impact.
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