Efficient learning of differential network in multi-source non-paranormal graphical models

TL;DR

This paper introduces an efficient method for learning differential networks in multi-source non-paranormal graphical models, improving speed and accuracy over existing techniques, especially in sparse settings, and demonstrating real-world applicability in cancer research.

Contribution

The paper proposes a novel, computationally efficient approach for differential network estimation that leverages multiple data sources and outperforms existing methods in speed and accuracy.

Findings

Outperforms previous methods in speed and accuracy.

Effectively infers differential networks from multi-source data.

Identifies genes related to drug resistance confirmed by independent studies.

Abstract

This paper addresses learning of sparse structural changes or differential network between two classes of non-paranormal graphical models. We assume a multi-source and heterogeneous dataset is available for each class, where the covariance matrices are identical for all non-paranormal graphical models. The differential network, which are encoded by the difference precision matrix, can then be decoded by optimizing a lasso penalized D-trace loss function. To this aim, an efficient approach is proposed that outputs the exact solution path, outperforming the previous methods that only sample from the solution path in pre-selected regularization parameters. Notably, our proposed method has low computational complexity, especially when the differential network are sparse. Our simulations on synthetic data demonstrate a superior performance for our strategy in terms of speed and accuracy compared to an existing method. Moreover, our strategy in combining datasets from multiple sources is shown to be very effective in inferring differential network in real-world problems. This is backed by our experimental results on drug resistance in tumor cancers. In the latter case, our strategy outputs important genes for drug resistance which are already confirmed by various independent studies.