Digital Public Defence: Zhi Zhao

Msc Zhi Zhao at Institute of Basic Medical Sciences will be defending the thesis “Multivariate structured penalized and Bayesian regressions for pharmacogenomic screens” for the degree of PhD (Philosophiae Doctor).

The public defence will be held as a video conference over Zoom.

The defence will follow regular procedure as far as possible, hence it will be open to the public and the audience can ask ex auditorio questions when invited to do so.

Click here to participate in the digital public defence

Download Zoom here


Digital Trial Lecture – time and place

See Digital Trial Lecture.

Adjudication committee

  • First opponent: Professor Anne-Laure Boulesteix, Ludwig-Maximilians-Universität München
  • Second opponent: Associate professor Francesco Claudio Stingo, University of Florence
  • Third member and chair of the evaluation committee: Group leader Marieke Kuijjer, University of Oslo

Chair of the Defence

Associate Professor Ragnhild Eskeland, University of Oslo

Principal Supervisor

Associate Professor Manuela Zucknick, University of Oslo


Pharmacogenomic screens for personalized cancer therapy are the focused biomedical application in this thesis. Due to the complex relationships between targeted cancer drugs and high-dimensional genomic predictors, we have developed penalized likelihood methods and Bayesian hierarchical models to capture the complex structures in the pharmacogenomic data and to predict drug sensitivity.

The first part of the thesis proposed to address the correlations between drug sensitivity measures for multiple cancer drugs and the heterogeneity of multiple sources of genomic data in multivariate penalized likelihood methods with structured penalities. The proposed methods can improve the prediction performance of drug sensitivity. The second part of the thesis exploited Bayesian priors for the relationships between multiple drugs and relationships between drug sensitivity and the targeted pathways or genes of cancer drugs. Large pharmocogenomic screens may also include samples from multiple cancer tissue types. We employed random effects to address the sample heterogeneity in the proposed Bayesian model. The results have shown good structure recovery in the complex data and good prediction of responses by the new Bayesian models.

Additional information

Contact the research support staff.

Published Sep. 25, 2020 1:09 PM - Last modified Oct. 12, 2020 10:44 AM