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Making sense of Omics

Statistics for Omics

Welcome to the site of the Statistics for Omics unit! Our aim is to link omics to clinical response by novel, problem-specific statistical methods.

As part of the Department of Epidemiology and Biostatistics of the VU University Medical Center, our unit is involved in consultancy, research and teaching. More information about who we are, our work and how to reach us can be found in these pages.

Statistics for Omics

Statistics

Networks                                                                                                                                                                              Our research generates methods to learn molecular network from omics data. In particular, identifying (parts of these) networks to be differential between disease stages is a first step towards network medicine.

Integrated analysis of omics datasets 
Our research also involves methods to unravel associations between different types of molecular profiles. These make use of many molecular features at the same time, and are ideal to be used in genome-wide studies.

Clinical prediction using co-data 
Omics data is 2 x Big data: a) number of features ánd b) sources of auxiliary data: co-data. We develop methods that jointly use co-data and the main data, rendering better predictions and markers for several applications.

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Omics

Omics data refers to the high-throughput quantification of some pool of molecular molecules. Often, these data have more features than observations. Our group provides statistical support for the processing and analysis of a wide variety of omics data, such as genomicmetabolomic, and microbiomic data. Our expertise ranges from microarrays to next-generation sequencing platforms for genomics, and includes various platforms for metabolomics and microbiomics.

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Software & Support

Research Support
Omics data analysis support is core business for our group. We supply tailored solutions for a variety of omics data analysis questions in the VUmc, covering study design, preprocessing and downstream analysis. Our focus is cancer genomics, but our support extends towards others diseases, like Alzheimer.

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Software
Software is the tool for disseminating our research. We have contributed >15 R packages (>30,000 downloads per year) to well-known public repositories like CRAN, Bioconductor and Github.

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Latest News

Vacancy: Biostatistician / Statistical machine learner

14-01-2020

We have a vacancy for an Assistant Professor "Biostatistics for High-Dimensional Data". 
Machine learners with affinity for biostatistics are also welcome to apply. 

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Accepted paper

09-12-2019

 

The paper: "Adaptive group-regularized logistic elastic net regression" by Magnus Münch et al. is accepted for publication Biostatistics!

 

 

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Grant

12-11-2019

We are proud to announce that the proposal "Co-data random forest learning for rare tumors" has been granted by the Hanarth Foundation for the call "Artificial Intelligence for Oncology".  

 

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