Data-Driven and Knowledge-Based Algorithms for Gene Network Reconstruction on High-Dimensional Data

Previous efforts in gene network reconstruction have mainly focused on data-driven modeling, with little attention paid to knowledge-based approaches. Leveraging prior knowledge, however, is a promising paradigm that has been gaining momentum in network reconstruction and computational biology research communities. This paper proposes two new algorithms for reconstructing a gene network from expression profiles with and without prior knowledge in small sample and high-dimensional settings. First, using tools from the statistical estimation theory, particularly the empirical Bayesian approach, the current research estimates a covariance matrix via the shrinkage method. Second, estimated covariance matrix is employed in the penalized normal likelihood method to select the Gaussian graphical model. This formulation allows the application of prior knowledge in the covariance estimation, as well as in the Gaussian graphical model selection. Experimental results on simulated and real datasets show that, compared to state-of-the-art methods, the proposed algorithms achieve better results in terms of both PR and ROC curves. Finally, the present work applies its method on the RNA-seq data of human gastric atrophy patients, which was obtained from the EMBL-EBI database. The source codes and relevant data can be downloaded from: https://github.com/AbbaszadehO/DKGN.