See also: IRC log
Kei: TCM dataset update
<ericP> hye all
Kei: Jun has updated the dataset, will update a new version into KB
(hi Eric :-) )
Kei: August 17th, presentation by
Chen
... Moving to the Microarray Use Case
<apaschke> http://esw.w3.org/topic/HCLSIG_BioRDF_Subgroup/QueryFederation2
http://esw.w3.org/topic/HCLSIG_BioRDF_Subgroup/QueryFederation2
Kei: Contacted the neuroscience
consortium, focusing on microarray experiments
... EBI hosts public microarray repository; US hosts the
GEO;
Most of the publish data in the neuroscience microarray consoroitum have been deposited in GEO
the website of the consoritum provides annotation on each of the microarray experiments, but the public repository does not have a lot of microarray annotation
http://np2.ctrl.ucla.edu/np2/viewProject.do?action=viewProject&projectId=433773
Alzheimer disease patients
this paper compares the gene expersssion profiles between normal individual and alzheimer disease patients, especially those patients with tangled cells
sattya has identified some experimental type of provenance
the paper also provides additional datasets
the datasets represents different experimental conditions and a small subset of genes has been found with over/under expressed genes
it would be interesting to find additional datasets on neural cell, namely pyramidal neurons
another level of integration would be to find comparable experiments
where a gene list could be extracted and common genes between the two datasets to be found
rules that make biological sense could be devised
the gene annotations could be potentially integrated with gene ID and also the samples
it would be interesting to compara gene annotation between experiments in order to create rules on certain genes that are over-expressed across the experiments
the microarray consortium, in addition to providing XML also provides MAGE-ML, a standardized XML format, proposed by the MGED
over time, the Committee also proposes MAGE-Tab, a reduced version of MAGE-ML
(although the consoritia only provides the MAGE-ML)
there is acollection of ontologies we could tap into,for example gene ontology, SWAN and NIFSTD for neurology
one option of converting this data to RDF would be to automatically convert the MAGE-ML into RDF using a general XML-to-RDF converter
satya: there are many tools to convert realtional databases to RDF
<apaschke> there are tools which consider the XML schema for the transformation to RDF and tools which do not consider the schema
satya: there is very fine level control of the data that converted to RDF in existing tools
<apaschke> we should also take a look at the XSD schema; maybe we also want to translate the XSD into a ontology for the XML microarry data into RDF data
can we convert a SPARQL query into an XML query much the same way that we do it for SQL?
RDAL: from an XML schema, an XSLT is generated, which is the GRDDLS transform
the most interesting scenario would be the dinamyc mapping from SPARQL to XML
what would be the best practice/recommendation for such a task?
Adrian: if the schema could be converted into an ontology, it would easily integrate with other ontologies, such as GO
how to represent the provenance information?
http://bioportal.bioontology.org/visualize/38801
(MGED ontology)
it would be good also to find interesting rules
<apaschke> using that microarray data can be intersting to use it in the RuleResponder use case, e.g. finding experts for Alzheimer disease http://ibis.in.tum.de/projects/paw/docs/ResponderHCLS_slides.pdf
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