Calibration of Personalized Patient Pharmacokinetic Models

M2 Mathsmod Internship repor

Significance Tests for Gaussian Graphical Models Based on Shrunken Densities

Gaussian Graphical Models (GGMs) are important probabilistic graphical models in Statistics. Inferring a GGM’s structure from data implies computing the inverse of the covariance matrix (i.e. the precision matrix). When the number of variables p is larger than the sample size n, the (sample) covariance estimator is not invertible and therefore another estimator is required. Covariance estimators based on shrinkage are more stable (and invertible), however, classical hypothesis testing for the ”shrunk” coefficients is an open challenge. In this paper, we present an exact null-density that naturally includes the shrinkage, and allows an accurate parametric significance test that is accurate and computationally efficient

Gene regulatory network reconstruction with prior knowledge over mRNA data for COPD patients and controls

Chronic obstructive pulmonary disease (COPD) is a type of lung disease characterized by persistent bronchitis and emphysema. Current therapy is restricted to alleviate lung tissue inflammation, but is not able to stabilize or improve lung function of patients making necessary to understand the underlying molecular mechanisms of COPD. Genome-wide gene expression of lung tissue provides a powerful tool to elucidate molecular mechanism of COPD patients. In particular, Bayesian Networks (BNs) have been applied to infer genetic regulatory interactions from microarray gene expression data. In this study we aim obtain a clearer understanding of the genes interaction in COPD patients by learning a BN over microarray expression data. A subset of genes was selected for the study fulfilling that i) the genes were significantly expressed in COPD stage 4 and ii) there is reported gene-gene experimental association. The reported associations are introduced as prior biological knowledge in the reconstruction