David Adametz

Learn More
A fully probabilistic approach to reconstructing Gaussian graphical models from distance data is presented. The main idea is to extend the usual central Wishart model in traditional methods to using a likelihood depending only on pairwise distances, thus being independent of geometric assumptions about the underlying Euclidean space. This extension has two(More)
Molecular classification of hepatocellular carcinomas (HCC) could guide patient stratification for personalized therapies targeting subclass-specific cancer 'driver pathways'. Currently, there are several transcriptome-based molecular classifications of HCC with different subclass numbers, ranging from two to six. They were established using resected(More)
Compensatory signaling pathways in tumors confer resistance to targeted therapy, but the pathways and their mechanisms of activation remain largely unknown. We describe a procedure for quantitative proteomics and phosphoproteomics on snap-frozen biopsies of hepatocellular carcinoma (HCC) and matched nontumor liver tissue. We applied this procedure to(More)
This paper considers a Bayesian view for estimating a sub-network in a Markov random field. The sub-network corresponds to the Markov blanket of a set of query variables, where the set of potential neighbours here is big. We factorize the posterior such that the Markov blanket is conditionally independent of the network of the potential neighbours. By(More)
  • 1