I’d like to draw your attention to two online data analysis challenges that both, in their way, address drug testing on animals and how results of such testing translate to human physiology.
CAMDA 2013 (12th international conference on critical assessment of massive data analysis) is a conference that focuses on massive data sets in the life sciences. This year, it has two associated analysis challenges, one of which is “prediction of drug compatibility from an extremely large toxicogenomic data set.” The data set used in this challenge contains over dataset contains over 20,000 genome expression microarrays, each measuring perhaps about 20,000 genes in the liver of rats treated with mainly human drugs. There are two questions that the organizers want to address:
- Question 1: Can we replace animal studies with in vitro assays? ["in vitro" literally means "in glass", for instance in a test tube]
- Question 2: Can we predict liver injury in humans using toxicogenomics data from animals?
Meanwhile, the SBV (systems biology verification) Improver project, which ran a prediction contest last year that was covered in this blog, is starting its new Species Translation Challenge, which also aims to address how “translatable” biological events in rats or mice are to humans. This challenge, which has four sub-challenges, aims to answer the following questions:
- Can the perturbations of signaling pathways in one species predict the response to a given stimulus in another species?
- Which biological pathways, functions and gene expression profiles are most robustly translated?
- Which gene expression profiles and associated biological pathways / functions are most robustly translated?
- Does translation depend on the nature of the stimulus or data type collected such as protein phosphorylation and cytokine responses?
- Which computational methods are most effective for inferring gene, phosphorylation and pathway response from one species to another?
I think it will be very interesting to see how these challenges play out and to compare their respective outcomes.