Graphical model estimation from modern multi-omics data requires a balance between statistical estimation performance and computational scalability. We introduce a novel pseudolikelihood-based graphical model framework that reparameterizes the target precision matrix while preserving sparsity pattern and estimates it by minimizing an -penalized empirical risk based on a new loss function. The proposed estimator maintains estimation and selection consistency in various metrics under high-dimensional assumptions. The associated optimization problem allows for a provably fast computation algorithm using a novel operator-splitting approach and communication-avoiding distributed matrix multiplication. A high-performance computing implementation of our framework was tested in simulated data with up to one million variables demonstrating complex dependency structures akin to biological networks. Leveraging this scalability, we estimated partial correlation network from a dual-omic liver cancer data set. The co-expression network estimated from the ultrahigh-dimensional data showed superior specificity in prioritizing key transcription factors and co-activators by excluding the impact of epigenomic regulation, demonstrating the value of computational scalability in multi-omic data analysis. %derived from the gene expression data.
View on arXiv@article{lee2025_2412.11554,
title={ Learning Massive-scale Partial Correlation Networks in Clinical Multi-omics Studies with HP-ACCORD },
author={ Sungdong Lee and Joshua Bang and Youngrae Kim and Hyungwon Choi and Sang-Yun Oh and Joong-Ho Won },
journal={arXiv preprint arXiv:2412.11554},
year={ 2025 }
}