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Manuela Zucknick

Professor in Biostatistics, Director of the Oslo Centre for Biostatistics and Epidemiology

University of Oslo
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Norway

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Professor Manuela Zucknick is a faculty member in the Department of Biostatistics at the University of Oslo, Norway. She serves as the Director of the Oslo Centre for Biostatistics and Epidemiology. Her research encompasses various areas including multivariate Bayesian methods in pharmacogenomics, survival modeling in omics data, and statistical analysis of RNA-Sequencing. She has also contributed to studies on maternal health impacts on fetal outcomes and developed tools for drug combination screen analysis.

Positions (2)

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Manuela Zucknick

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University of Oslo

PhD Position in Biostatistics at the Oslo Centre for Biostatistics and Epidemiology (OCBE)

The University of Oslo invites applications for a 3-year PhD Fellowship in Biostatistics at the Oslo Centre for Biostatistics and Epidemiology (OCBE), Institute of Basic Medical Sciences. OCBE is one of Europe's most active biostatistics centres, internationally recognized for its research in biostatistics, machine learning, and epidemiology, and maintains numerous collaborations with leading biomedical research groups both in Norway and internationally. The successful candidate will join the research group 'Statistical learning in molecular medicine' and participate in the T-PRESS consortium, which aims to support trustworthy personalized healthcare decisions. The PhD project, entitled “Reliable Bayesian prediction models with uncertainty quantification for trustworthy personalized treatment decisions in the T-PRESS Evidence Ecosystem Framework,” focuses on developing Bayesian statistical and machine learning methods for treatment response prediction in cancer, specifically using adjuvant immunotherapy for renal cell carcinoma as a use case. The research will integrate clinical information, molecular tumor characterizations, and in vitro drug response experiments with patient-derived organoids (PDOs) to provide personalized risk assessments and predictions for treatment effect, including explicit uncertainty estimates. Key research questions include adapting statistical models for drug response, integrating heterogeneous clinical and multi-omics data, evaluating the predictive value of organoid data, and validating models for trustworthy clinical decision support. The PhD program at the University of Oslo is designed for research training leading to a PhD degree in medicine and health sciences. Candidates are expected to publish several papers in leading scientific journals and complete a small number of advanced courses as part of their doctoral training. Admission to the doctoral program must be approved within three months of appointment. Applicants must hold a master's degree in biostatistics, bioinformatics, statistics, mathematics, computer science, or a related quantitative field with proven competence in statistics. Ideal candidates will have a strong background in statistical methodology, especially Bayesian statistics and statistical learning for high-dimensional data, solid programming skills (R or Python), and a keen interest in interdisciplinary research involving molecular biology and cancer. English proficiency is mandatory; Norwegian language skills are not required. The University of Oslo values diversity and inclusion and encourages applicants from varied backgrounds, including those with disabilities or CV gaps. The position offers a stimulating international research environment, meaningful tasks contributing to societal development, good welfare schemes, and career development opportunities. Salary ranges from NOK 550,800 to 595,000 per year, with membership in Statens Pensjonskasse providing additional benefits. Applications must be submitted via Jobbnorge, including a cover letter, CV, educational certificates and transcripts, documentation of English proficiency (if applicable), list of publications (if applicable), and 2-3 references. All documentation should be in English or a Scandinavian language. The deadline for applications is 17th May 2026. For questions about the position, contact Professor Manuela Zucknick at [email protected]. For more information about the University of Oslo and OCBE, visit uio.no .

3 months ago

Articles (13)

Multivariate Bayesian structured variable selection for pharmacogenomic studies

Cancer drug sensitivity screens combined with multi-omics characterisation of the cancer cells have become an important tool to determine the optimal treatment for each patient. We propose a multivariate Bayesian structured variable selection model for sparse identification of multi-omics features associated with multiple correlated drug responses. Our model uses known structure between drugs and their targeted genes via a Markov random field (MRF) prior in sparse seemingly unrelated regression. The use of MRF prior can improve the model performance compared to other common priors. The proposed model is applied to the Genomics of Drug Sensitivity in Cancer data.

Year:

2023

Adjustment of spurious correlations in co-expression measurements from RNA-Sequencing data

Motivation Gene co-expression measurements are widely used in computational biology to identify coordinated expression patterns across a group of samples. Coordinated expression of genes may indicate that they are controlled by the same transcriptional regulatory program, or involved in common biological processes. Gene co-expression is generally estimated from RNA-Sequencing data, which are commonly normalized to remove technical variability. Here, we demonstrate that certain normalization methods, in particular quantile-based methods, can introduce false-positive associations between genes. These false-positive associations can consequently hamper downstream co-expression network analysis. Quantile-based normalization can, however, be extremely powerful. In particular, when preprocessing large-scale heterogeneous data, quantile-based normalization methods such as smooth quantile normalization can be applied to remove technical variability while maintaining global differences in expression for samples with different biological attributes. Results We developed SNAIL (Smooth-quantile Normalization Adaptation for the Inference of co-expression Links), a normalization method based on smooth quantile normalization specifically designed for modeling of co-expression measurements. We show that SNAIL avoids formation of false-positive associations in co-expression as well as in downstream network analyses. Using SNAIL, one can avoid arbitrary gene filtering and retain associations to genes that only express in small subgroups of samples. This highlights the method’s potential future impact on network modeling and other association-based approaches in large-scale heterogeneous data. Availability and implementation The implementation of the SNAIL algorithm and code to reproduce the analyses described in this work can be found in the GitHub repository https://github.com/kuijjerlab/PySNAIL.

Year:

2023

Collaborators (11)

Ellen Gabrielsen Hjelle

Associate Professor

OsloMet – Oslo Metropolitan University

NORWAY

Thomas Fleischer

-

NORWAY

simon rayner

Professor

University of Oslo

NORWAY

Tero Aittokallio

University of Helsinki

FINLAND

Jorrit M Enserink

Oslo University Hospital

NORWAY

Arvind Y.M. Sundaram

University of Oslo

NORWAY

Gabriele Kitzmüller

Associate professor, PHD

UiT Norges arktiske universitet - Campus Narvik

NORWAY

Geir K Sandve

University of Oslo

NORWAY

Eivind Hovig

Professor/ Head, Center of Bioinformatics

University of Oslo

NORWAY

Theresa L. Powell

-

UNITED STATES

Marieke Lydia Kuijjer

University of Oslo

NORWAY
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