Search

Your search keyword '"Adametz, David"' showing total 16 results
Start Over Author "Adametz, David"
16 results on '"Adametz, David"'

1. Bayesian Markov Blanket Estimation

Author

Kaufmann, Dinu, Parbhoo, Sonali, Wieczorek, Aleksander, Keller, Sebastian, Adametz, David, and Roth, Volker

Subjects
Statistics - Machine Learning, Computer Science - Learning
Abstract

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 exploiting this blockwise decoupling, we derive analytic expressions for posterior conditionals. Subsequently, we develop an inference scheme which makes use of the factorization. As a result, estimation of a sub-network is possible without inferring an entire network. Since the resulting Gibbs sampler scales linearly with the number of variables, it can handle relatively large neighbourhoods. The proposed scheme results in faster convergence and superior mixing of the Markov chain than existing Bayesian network estimation techniques., Comment: 16 pages, 5 figures

Published
2015

3. Information Bottleneck for Pathway-Centric Gene Expression Analysis

Author

Adametz, David, Rey, Mélanie, Roth, Volker, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Jiang, Xiaoyi, editor, Hornegger, Joachim, editor, and Koch, Reinhard, editor

Published
2014
Full Text
View/download PDF

5. Gene expression analysis of biopsy samples reveals critical limitations of transcriptome‐based molecular classifications of hepatocellular carcinoma

Author

Makowska, Zuzanna, Boldanova, Tujana, Adametz, David, Quagliata, Luca, Vogt, Julia E., Dill, Michael T., Matter, Mathias S., Roth, Volker, Terracciano, Luigi, and Heim, Markus H.

Subjects
molecular classification, gene expression, Original Article, Original Articles, hepatocellular carcinoma, transcriptome, digestive system diseases
Abstract

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 tumours that introduce a selection bias towards patients without liver cirrhosis and with early stage HCCs. We generated and analyzed gene expression data from paired HCC and non‐cancerous liver tissue biopsies from 60 patients as well as five normal liver samples. Unbiased consensus clustering of HCC biopsy profiles identified 3 robust classes. Class membership correlated with survival, tumour size and with Edmondson and Barcelona Clinical Liver Cancer (BCLC) stage. When focusing only on the gene expression of the HCC biopsies, we could validate previously reported classifications of HCC based on expression patterns of signature genes. However, the subclass‐specific gene expression patterns were no longer preserved when the fold‐change relative to the normal tissue was used. The majority of genes believed to be subclass‐specific turned out to be cancer‐related genes differentially regulated in all HCC patients, with quantitative rather than qualitative differences between the molecular subclasses. With the exception of a subset of samples with a definitive β‐catenin gene signature, biological pathway analysis could not identify class‐specific pathways reflecting the activation of distinct oncogenic programs. In conclusion, we have found that gene expression profiling of HCC biopsies has limited potential to direct therapies that target specific driver pathways, but can identify subgroups of patients with different prognosis.

Published
2016

6. Bayesian Markov Blanket Estimation

Author

Kaufmann, Dinu, Parbhoo, Sonali, Wieczorek, Aleksander, Keller, Sebastian, Adametz, David, and Roth, Volker

Subjects
FOS: Computer and information sciences, Computer Science - Learning, Statistics - Machine Learning, Machine Learning (stat.ML), Machine Learning (cs.LG)
Abstract

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 exploiting this blockwise decoupling, we derive analytic expressions for posterior conditionals. Subsequently, we develop an inference scheme which makes use of the factorization. As a result, estimation of a sub-network is possible without inferring an entire network. Since the resulting Gibbs sampler scales linearly with the number of variables, it can handle relatively large neighbourhoods. The proposed scheme results in faster convergence and superior mixing of the Markov chain than existing Bayesian network estimation techniques., Comment: 16 pages, 5 figures

Published
2016

7. Recovering networks from distance data

Author

Prabhakaran, Sandhya, Adametz, David, Metzner, Karin, Böhm, Alexander, Roth, Volker, Prabhakaran, Sandhya, Adametz, David, Metzner, Karin, Böhm, Alexander, and Roth, Volker

Abstract

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 advantages: the model becomes invariant against potential bias terms in the measurements, and can be used in situations which on input use a kernel- or distance matrix, without requiring direct access to the underlying vectors. The latter aspect opens up a huge new application field for Gaussian graphical models, as network reconstruction is now possible from any Mercer kernel, be it on graphs, strings, probabilities or more complex objects. We combine this likelihood with a suitable prior to enable Bayesian network inference. We present an efficient MCMC sampler for this model and discuss the estimation of module networks. Experiments depict the high quality and usefulness of the inferred networks

Published
2018

9. Invariances for Gaussian models

Author

Adametz, David, Roth, Volker, and Vetter, Thomas

Abstract

At the heart of a statistical analysis, we are interested in drawing conclusions about random variables and the laws they follow. For this we require a sample, therefore our approach is best described as learning from data. In many instances, we already have an intuition about the generating process, meaning the space of all possible models reduces to a specific class that is defined up to a set of unknown parameters. Consequently, learning becomes the task of inferring these parameters given observations. Within this scope, the thesis answers the following two questions: Why are invariances needed? Among all parameters of the model, we often distinguish between those of interest and the so-called nuisance. The latter does not carry any meaning for our purposes, but may still play a crucial role in how the model supports the parameters of interest. This is a fundamental problem in statistics which is solved by finding suitable transformations such that the model becomes invariant against unidentifiable properties. Often, the application at hand already decides upon the necessary requirements: a Euclidean distance matrix, for example, does not carry translational information of the underlying coordinate system. Why Gaussian models? The normal distribution constitutes an important class in statistics due to frequent occurrences in nature, hence it is highly relevant for many research disciplines including physics, astronomy, engineering, but also psychology and social sciences. Besides fundamental results like the central limit theorem, a significant part of its appeal is rooted in convenient mathematical properties which permit closed-form solutions to numerous problems. In this work, we develop and discuss generalizations of three established models: a Gaussian mixture model, a Gaussian graphical model and the Gaussian information bottleneck. On the one hand, all of these are analytically convenient, but on the other hand they suffer from strict normality requirements which seriously limit their range of application. To this end, our focus is to explore solutions and relax these restrictions. We successfully show that with the addition of invariances, the aforementioned models gain a substantial leap forward while retaining their core concepts of the Gaussian foundation.

Published
2015
Full Text
View/download PDF

10. Podnikatelský záměr – založení a provoz crossfitové posilovny

Author

Heralecký, Tomáš, Adametz, David, Klinger, Erik, Heralecký, Tomáš, Adametz, David, and Klinger, Erik

Abstract

Tato bakalářská práce se zabývá podnikatelským záměrem na založení a provoz crossfitové posilovny. Práce se skládá ze dvou částí, teoretické, analytické a praktické. V teoretické části jsou popsány cíle práce a definovány základní pojmy. V analytické části je za pomocí teoretických poznatků analyzována současná situace na trhu. V praktické části je samotná realizace podnikatelského záměru., This Bachelor thesis deals with the business plan for the establishment and operation of crossfit gym. The Bachelor thesis is divided into two parts, theoretical, analytical and practical. In the theoretical part are described aims and defined basic terms. In the analytical part, the current market situation is analyzed using theoretical knowledge. In the practical part is the actual implementation of the business plan.

11. Podnikatelský záměr – založení a provoz crossfitové posilovny

Author

Heralecký, Tomáš, Adametz, David, Heralecký, Tomáš, and Adametz, David

Abstract

Tato bakalářská práce se zabývá podnikatelským záměrem na založení a provoz crossfitové posilovny. Práce se skládá ze dvou částí, teoretické, analytické a praktické. V teoretické části jsou popsány cíle práce a definovány základní pojmy. V analytické části je za pomocí teoretických poznatků analyzována současná situace na trhu. V praktické části je samotná realizace podnikatelského záměru., This Bachelor thesis deals with the business plan for the establishment and operation of crossfit gym. The Bachelor thesis is divided into two parts, theoretical, analytical and practical. In the theoretical part are described aims and defined basic terms. In the analytical part, the current market situation is analyzed using theoretical knowledge. In the practical part is the actual implementation of the business plan.

12. Podnikatelský záměr – založení a provoz crossfitové posilovny

Author

Heralecký, Tomáš, Adametz, David, Heralecký, Tomáš, and Adametz, David

Abstract

Tato bakalářská práce se zabývá podnikatelským záměrem na založení a provoz crossfitové posilovny. Práce se skládá ze dvou částí, teoretické, analytické a praktické. V teoretické části jsou popsány cíle práce a definovány základní pojmy. V analytické části je za pomocí teoretických poznatků analyzována současná situace na trhu. V praktické části je samotná realizace podnikatelského záměru., This Bachelor thesis deals with the business plan for the establishment and operation of crossfit gym. The Bachelor thesis is divided into two parts, theoretical, analytical and practical. In the theoretical part are described aims and defined basic terms. In the analytical part, the current market situation is analyzed using theoretical knowledge. In the practical part is the actual implementation of the business plan.

13. Podnikatelský záměr – založení a provoz crossfitové posilovny

Author

Heralecký, Tomáš, Adametz, David, Heralecký, Tomáš, and Adametz, David

Abstract

Tato bakalářská práce se zabývá podnikatelským záměrem na založení a provoz crossfitové posilovny. Práce se skládá ze dvou částí, teoretické, analytické a praktické. V teoretické části jsou popsány cíle práce a definovány základní pojmy. V analytické části je za pomocí teoretických poznatků analyzována současná situace na trhu. V praktické části je samotná realizace podnikatelského záměru., This Bachelor thesis deals with the business plan for the establishment and operation of crossfit gym. The Bachelor thesis is divided into two parts, theoretical, analytical and practical. In the theoretical part are described aims and defined basic terms. In the analytical part, the current market situation is analyzed using theoretical knowledge. In the practical part is the actual implementation of the business plan.

14. Podnikatelský záměr – založení a provoz crossfitové posilovny

Author

Heralecký, Tomáš, Adametz, David, Klinger, Erik, Heralecký, Tomáš, Adametz, David, and Klinger, Erik

Abstract

Tato bakalářská práce se zabývá podnikatelským záměrem na založení a provoz crossfitové posilovny. Práce se skládá ze dvou částí, teoretické, analytické a praktické. V teoretické části jsou popsány cíle práce a definovány základní pojmy. V analytické části je za pomocí teoretických poznatků analyzována současná situace na trhu. V praktické části je samotná realizace podnikatelského záměru., This Bachelor thesis deals with the business plan for the establishment and operation of crossfit gym. The Bachelor thesis is divided into two parts, theoretical, analytical and practical. In the theoretical part are described aims and defined basic terms. In the analytical part, the current market situation is analyzed using theoretical knowledge. In the practical part is the actual implementation of the business plan.

15. Recovering networks from distance data

Author

Prabhakaran, Sandhya, Adametz, David, Metzner, Karin, Böhm, Alexander, Roth, Volker, Prabhakaran, Sandhya, Adametz, David, Metzner, Karin, Böhm, Alexander, and Roth, Volker

Abstract

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 advantages: the model becomes invariant against potential bias terms in the measurements, and can be used in situations which on input use a kernel- or distance matrix, without requiring direct access to the underlying vectors. The latter aspect opens up a huge new application field for Gaussian graphical models, as network reconstruction is now possible from any Mercer kernel, be it on graphs, strings, probabilities or more complex objects. We combine this likelihood with a suitable prior to enable Bayesian network inference. We present an efficient MCMC sampler for this model and discuss the estimation of module networks. Experiments depict the high quality and usefulness of the inferred networks

Catalog

Books, media, physical & digital resources
See catalog results