A comparative study of covariance selection models for the inference of gene regulatory networks.

Motivation: The inference, or 'reverse-engineering', of gene regulatory networks from expression data and the description of the complex dependency structures among genes are open issues in modern molecular biology.

Results: In this paper we compared three regularized methods of covariance selection for the inference of gene regulatory networks, developed to circumvent the problems raising when the number of observations n is smaller than the number of genes p. The examined approaches provided three alternative estimates of the inverse covariance matrix: (a) the 'PINV' method is based on the Moore-Penrose pseudoinverse, (b) the 'RCM' method performs correlation between regression residuals and (c) 'ℓ(2C)' method maximizes a properly regularized log-likelihood function.

Activated innate immunity and the involvement of CX3CR1-fractalkine in promoting hematuria in patients with IgA nephropathy.

A hallmark of immunoglobulin A nephropathy (IgAN) is episodes of gross hematuria coinciding with mucosal infections that can represent the disease-triggering event. Here we performed a whole genomic screen of IgAN patients during gross hematuria to clarify the link between mucosal antigens and glomerular hematuria. Modulated genes showed a clear involvement of the intracellular interferon signaling, antigen-presenting pathway, and the immunoproteasome.