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- David Adametz, Volker Roth
- NIPS
- 2011

A Bayesian approach to partitioning distance matrices is presented. It is inspired by the Translation-invariant Wishart-Dirichlet process (TIWD) in [1] and shares a number of advantageous properties like the fully probabilistic nature of the inference model, automatic selection of the number of clusters and applicability in semi-supervised settings. In… (More)

- Sandhya Prabhakaran, David Adametz, Karin J. Metzner, Alexander Böhm, Volker Roth
- Machine Learning
- 2012

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)

- Zuzanna Makowska, Tujana Boldanova, +7 authors Markus H. Heim
- The journal of pathology. Clinical research
- 2016

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)

- Eva Dazert, Marco Colombi, +8 authors Michael N Hall
- Proceedings of the National Academy of Sciences…
- 2016

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)

- David Adametz, Mélanie Rey, Volker Roth
- GCPR
- 2014

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)

- David Adametz, Volker Roth
- NIPS
- 2014

We present an inference method for Gaussian graphical models when only pairwise distances of n objects are observed. Formally, this is a problem of estimating an n× n covariance matrix from the Mahalanobis distances dMH(xi,xj), where object xi lives in a latent feature space. We solve the problem in fully Bayesian fashion by integrating over the… (More)

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