Designing Microarray Experiments

Sebastiani, Paola; Milton, Jacqui

doi:10.1007/978-0-387-09760-2_13

Search citation statements

Order By: Relevance

Paper Sections

Select...

Causality In Network1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2018

Publication Types

Select...

Book1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

(1 citation statement)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[25]. With the exception of courthouse business, most causal questions -at least in science -are concerned with EoC via adequate designs of experiments [122]. CoE questions are hindered by situations where appropriate confounding effects are difficult to disentangle, perhaps due to missing information (for example missing variables [117]).…”

Section: Causality In Networkmentioning

confidence: 99%

Causal Queries from Observational Data in Biological Systems via Bayesian Networks: An Empirical Study in Small Networks

White

Vignes

2018

Methods in Molecular Biology

View full text Add to dashboard Cite

Biological networks are a very convenient modelling and visualisation tool to discover knowledge from modern high-throughput genomics and postgenomics data sets. Indeed, biological entities are not isolated, but are components of complex multi-level systems. We go one step further and advocate for the consideration of causal representations of the interactions in living systems. We present the causal formalism and bring it out in the context of biological networks, when the data is observational. We also discuss its ability to decipher the causal information flow as observed in gene expression. We also illustrate our exploration by experiments on small simulated networks as well as on a real biological data set.

show abstract

Section: Causality In Networkmentioning

confidence: 99%