[Forum SIS] Seminar @Chalmers/GU: "Bayesian modelling of treatment response in ex vivo drug screens for precision cancer medicine", Manuela Zucknick, 30 March

[Umberto Picchini]

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vi segnalo il seminario di cui in oggetto.

Saluti

Umberto Picchini

-- 
_________________________________________________________
Umberto Picchini, Associate Professor, PhD, Docent
https://umbertopicchini.github.io/  , twitter: @uPicchini

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You are welcome to the next Statistics seminar at Dept of Mathematical 
Sciences at Chalmers and Göteborg University.

We are very glad to have, on Tuesday 30 
March,<https://www.med.uio.no/imb/english/people/aca/manuelkz/>​Manuela 
Zucknick <https://www.med.uio.no/imb/english/people/aca/manuelkz/> 
(University of Oslo) who will talk on

/*Bayesian modelling of treatment response in ex vivo drug screens for 
precision cancer medicine*/
*//*

Zoom: https://chalmers.zoom.us/j/68401421726

Password: 329742

When: 14.15-15.15 CET ( = GMT + 1hr) , 30 March

Feel free to circulate this invitation in your network.

*​Abstract *
*Large-scale cancer pharmacogenomic screening experiments profile cancer 
cell lines or patient-derived cells versus hundreds of drug compounds. 
The aim of these in vitro studies is to use the genomic profiles of the 
cell lines together with information about the drugs to predict the 
response to a particular combination therapy, in particular to identify 
combinations of drugs that act synergistically. The field is hyped with 
rapid development of sophisticated high-throughput miniaturised 
platforms for rapid large-scale screens, but development of statistical 
methods for the analysis of resulting data is lagging behind. I will 
discuss typical challenges for estimation and prediction of response to 
combination therapies, from large technical variation and experimental 
biases to modelling challenges for prediction of drug response using 
genomic data. I will present two Bayesian models that we have recently 
developed to address diverse problems relating to the estimation and 
prediction tasks, and show how they can improve the identification of 
promising drug combinations over standard non-statistical approaches.


About the speaker
**
Manuela Zucknick is associate professor at Department of Biostatistics 
at University of Oslo. She is interested in Statistical Learning for 
translational and clinical cancer research, in particular for 
personalised cancer therapies. Integration of multi-omics data and other 
data sources for prediction of drug response and synergies in 
pharmacogenomic screensand of prognosis and treatment response in patients.

  * Statistical machine learning in molecular medicine
  * Structured high-dimensional regressions (for "large p, small n")
  * Regularization (using penalized likelihood and Bayesian methods)
  * Bayesian methods for integrating heterogeneous data sources (e.g.
    multi-omics) and for incorporating prior biological knowledge in
    risk prediction models


*

-- 
_________________________________________________________
Umberto Picchini, Associate Professor, PhD, Docent
https://umbertopicchini.github.io/  , twitter: @uPicchini

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