ETH :: D-MATH :: ZüKoSt (applied seminar)

Functional genomics has a long tradition of inferring the inner working of a cell through analysis of its response to various perturbations.
Observing cellular features after knocking out or silencing a gene reveals which genes are essential for an organism or for a particular
pathway. A key obstacle to inferring genetic networks from perturbation screens is that phenotypic profiles generally offer only
indirect information on how genes interact.

I will discuss a network inference method that we called Nested Effects Models (NEM). It can be used to model the flow of information
in cells based on the nested structure of downstream effects of perturbations like RNAi mediated gene knockdowns.
Special attention will be given to strategies for controlling network complexity. I will demonstrate the power of our method in the
context of modelling disrupted Wnt signalling in colorectal cancers.

Speakers:

Rainer Spang (Universität Regensburg)

15-mar-2012 (thu)

Sander Greenland

Causal Inference: Much More than Just Statistics (at the University Zurich, Rämistrasse 73)

16:15-17:30

RAK E 8

Abstract:

There has been an explosion of statistical techniques labeled "causal inference methods," in
which the statistical analysis model is at least partly derived from a formal causal model.
Discussions of these methods may leave the impression that they solve a unique and general
problem of causal inference. That is not the case: Formal causal inference methods thus far focus
on idealized special cases in which the only available evidence comprises one study or a set of
similar studies, whereas there are usually diverse evidence sources that must be merged to form
credible inferences. This reality is addressed by informal "criterion" or "checklist" approaches
typified by Bradford Hill's nine causal considerations. Attempts to at least partially formalize
causal considerations have as yet had limited contact with formal causal modeling, although
those considerations can be formalized and merged with causal modeling to produce predictive
theories of causal inference. Nonetheless, the effort required for integrative approaches
combined with pressures to claim inferences from single evidence sources remain formidable
obstacles to implementation of those approaches

Speakers:

Sander Greenland (University of California, Los Angeles (UCLA))

19-apr-2012 (thu)

Sen Bodhisattva

tba

16:15-17:30

HG G 19.1

Abstract:

tba

Speakers:

Sen Bodhisattva (University of Cambridge, Cambridge UK)

10-may-2012 (thu)

Björn Bornkamp

tba

16:15-17:30

HG G 19.1

Abstract:

tba

Speakers:

Björn Bornkamp (Technische Universität Dortmund)

Further information: sekretariat@stat.math.ethz.ch
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