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BioRDF Update

BioRDF Update. Kei Cheung, Ph.D. Yale Center for Medical Informatics. CSHALS 2010: HCLS Tutorial, Boston, February 23, 2010. Current participants. Kei Cheung (Yale University) Helena Deus (University of Texas) Don Doherty (Brainstage) Rob Frost (Vector C)

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BioRDF Update

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  1. BioRDF Update Kei Cheung, Ph.D. Yale Center for Medical Informatics CSHALS 2010: HCLS Tutorial, Boston, February 23, 2010

  2. Current participants Kei Cheung (Yale University) Helena Deus (University of Texas) Don Doherty (Brainstage) Rob Frost (Vector C) Scott Marshall (University of Amsterdam) Michael Miller (Teranode) Adrian Paschke (Freie Universitat Berlin) Eric Prud'hommeaux (W3C) Satya Sahoo (Wright State University) Matthias Samwald (DERI and Konrad Lorenz Institute) Jun Zhao (Oxford University)

  3. Current tasks Query Federation Semantic integration of neuroscience microarray data and related data Expansion of previous query federation work (Cheung et al. A journey to semantic web query federation in the life sciences. BMC Bioinformatics. 10(Suppl 10):S10, 2009) Traditional Chinese Medicine (TCM) Collaboration with LODD Linking TCM data and other types of data including drug data

  4. Query federation in the context of neuroscience microarray data

  5. Gene expression in the neurosciences Gene expression may be an indicator of how well someone is aging The New England Centenarian Study DNA microarray technology allows scientists to scan tens of thousands of genes from a single sample at a time and then link them to specific biological functions

  6. Microarray examples/use cases NIH Neuroscience Microarray Consortium and EBI ArrayExpress RDF representation of experiment metadata and gene lists including provenance

  7. Representative concepts Disease (e.g., AD, PD) Neuron (e.g., dopamine neuron) Brain region (e.g., hippocampus, posterior cingulate cortex, visual cortex) Brain function (e.g., unimodal and heteromodal sensory association) Organism (e.g., human) Experimental factor (e.g., normal vs. AD) Sample extraction method (e.g., laser-capture microdissection) Proteins (e.g., NFT) Genes (e.g., gene lists) Biological process (e.g., energy metabolism) Cellular component (e.g., mitochondrial electron transport chain) Age Disease state Treatment

  8. Approach Reuse existing ontological terms and relationships (e.g., OBO Relation Ontology) Use Provenir ontology and aTags Create RDF representation of gene lists

  9. RDF representation of gene expression lists Genelist 1 Genelist 2 Genelist 3 Gene-specific annotation Sample-specific gene expression values Aggregated gene expression values

  10. RDF graph probeid “225871_at” “Entorhinal Cortex” gene1 symbol Brain_region “STEAP2” sample1 “AD” Disease_status name “six transmembrane epithelial antigen of prostate 2” Organism “Human” 503.7 value Expression_value_for_gene1_sample1_pair context “Signal” value “P” Expression_value_for_gene1_sample1_pair context “Detection”

  11. Live to 100! Featured Blog (from Ask Dr. Mao)Feeling like the absent-minded professor lately? Ginkgo, the oldest surviving species of tree, has been traced back 300 million years and is one of the most widely studied plants. The leaf of the ginkgo tree is shaped like a human brain, and some believe this is why, in Asia, it has always had a reputation of benefiting the mental processes. A dwindling memory and decreased concentration is largely caused by decreased blood flow to the brain and loss of brain cells; ginkgo has been confirmed to boost circulation to the brain and other organs, improving memory and cognitive functions. Additionally, ginkgo is used far and wide as a longevity tonic in Asia and Europe. The best-known and most commonly available form of ginkgo is as teas and herbal extracts, but ginkgo nut, used in the culinary traditions of Asian cultures, also has therapeutic properties and is also said to strengthen lung function.

  12. TCM project milestone Collaboration between BioRDF and LODD A paper was recently submitted to BMC Chinese Medicine (Thematic Series: Semantic Web for Chinese Medicine) Samwald et al. Integrating findings from traditional medicine into modern pharmaceutical research through semantic technologies Linking a variety of data involving herbs that have been studied in terms of their potential therapeutic effects on depression Data sources: TCMGeneDIT, PubMed, DBPedia, PharmGKB Semantic Web technologies: aTag (including an aTAG explorer), RDFa, and SPARQL endpoint

  13. Future plan Federate microarray data with other types of data including data stored in HCLS KB’s (e.g., pathway data and disease/phenotype data) Explore a range of federated queries Demos (e.g., iPhone application) Expand the TCM project

  14. The End

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