1 / 20

Service-enabling Biomedical Research Enterprise

Service-enabling Biomedical Research Enterprise. Chapter 5 B. Ramamurthy. Introduction. Life sciences have witnessed a flurry of innovations triggered by sequencing of human genome as well as genomes of other genomes.

melody
Download Presentation

Service-enabling Biomedical Research Enterprise

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Service-enabling Biomedical Research Enterprise Chapter 5 B. Ramamurthy

  2. Introduction • Life sciences have witnessed a flurry of innovations triggered by sequencing of human genome as well as genomes of other genomes. • Area of transformational medicine aims to improve communication between basic and clinical science to allow more therapeutic and diagnostic insights.

  3. Translational medicine • From bench to bedside • Exchange ideas, information and knowledge across organizational, governance, socio-cultural, political and national boundaries. • Currently mediated by the internet and exponentially-increasing resources • Digital resources: scientific literature, experimental data, curated annotation (metadata) human and machine generated. Ex: BlastSearches NCBI taxonomy

  4. Driving principles • Key requirements: large volume of data to be managed. How? • Transform to • Digital • Machine readable • Capable of being filtered • Aggregated • Transformed automatically • Context information: use and meaning along with content • Knowledge integration: combines data from research in mouse genetics, cell bilogy, animal neuropsychology, protein biology, neuropathology, and other areas. • Attention to drug discovery, systems bilogy and personalized medicine that rely heavily on integrating and interpreting data produced by experiments. • Heterogenious data

  5. BioSem Enterprise Architecture search Transform results Ex: integrate, generate metadata Dissemination Of results Clinical experiments Ex: drug discovery Diagnostic tools Research Knowledge Ex: Blast Clinical data Ex: JNI ontology Academic Knowledge Ex: cell, psychology molecular Treatment methods

  6. Use case • Parkinson’s disease (PD): • System physiology perspective • Cellular and molecular biology perspective • Pharmacology relating to chemical compounds that bind to receptors • Example query: show me the neuronal components that bind to a ligand which is a therapeutic agent in Parkinson’s disease in reach of the dopaminergic neurons in the substania nigra. • Domain specific shared semantics and classifications • Ontologies can help map among the domains and support seamless integration and interoperation.

  7. Development of Ontologies • Manual interaction between ontologists in experts • Textual descriptions are used for adding to this base • Link pre-existing ontologies for extensive coverage

  8. Ontology design and creation Approach (fig. 5.1) Subject matter Knowledge (Text) Identify core terms And phrases Map phrases to Relationship between classes Model terms using ontological Constructs: classes, properties Arrange classes and relationships in subsumption hierarchies Information queries Identify new classes and relationships Refine subsumption hierarchies Pre-existing classifications And ontologies Re-use classes and relationships Extenf subsumption hierarchies

  9. Identifying concepts and hierarchies • Text describing PD in p.105 • Study the analysis • Based on the analysis identify important ontological concepts relevant to PD: • Genes • Proteins • Genetic mutations • Diseases • See fig. 5.2 • Next step is to identify relationship among concepts

  10. Identifying and extracting relationships

  11. Extending the ontology based on information queries • Consider various queries and identify concepts and relationships needed to be part of PD ontology. • These concepts are needed to retrieve information and knowledge from the system. • This lead to additional new concepts. See fig.5.4

  12. PD: adding concepts to support information queries

  13. Ontology Re-use • It is desirable to re-use the ontology and vocabulary developed in the healthcare and life-sciences fields. • Diseases: PD information can be used in Huntington’s and Alzeimer’s. PD can reuse information from International classification of diseases ICD and its subset SNOMED. • Genes: more genes and genomic concepts such as proteins, pathways are added to ontologies. Consider connecting to Gene Ontology. • Neurological concepts: Consider using Neuro names 2007. • Enzymes: concepts related to enzymes and other chemicals may be required; you may use Enzyme Nomenclature 2007 • Be aware of inconsistencies and circularities. • Multiple models may emerge; choice should be based on use cases and functional requirements.

  14. Data sources • Now answering the question that we posted in slide#6, three data sources need to be integrated: • Neuron database, PDSP KI database, PubChem

  15. Data Integration • A centralized approach where data available through web based interfaces is converted into RDF and stored in a centralized repository • A federated approach where data continues to reside in the existing repositories. RDF mediator converts underlying data into RDF format. • RDF allows for focus on logical structures of information in contrast to only representational format (XML) or storage format (relational).

  16. Mapping ontological concepts to RDF graphs • Sample query discussed earlier results in these concepts: • Compartment located_on Neuron • Receptor located_in Compartment • Ligand binds_to Receptor • Ligand associated_with Disease • Next task to map these into RDF maps in the underlying data sources. • Using ontological definitions, data sources, SPARQL queries, and name space, RDF graphs are extracted.

  17. Generation and merging of RDF graphs D1 UR14 Parkinson’s disease UR16 D_Neuron UR12 Neuron Database type associated_with binds_to Neuron UR12 D1 UR14 5-H Tryptamine UR15 5-H Tryptamine UR15 Located_in D_Dendrite UR12 Located_in PDSPKI Database PubChem database

  18. Integrated RDF graph Parkinson’s disease UR16 D_Neuron UR12 type associated_with Neuron UR12 5-H Tryptamine UR15 Located_in binds_to D1 UR14 D_Dendrite UR12 Located_in

  19. Exam question? • Consider the PD case study that used ontological approach to querying distributed databases. • Discuss 10 reasons of using this approach as opposed to common SQL query and relational database approach. • Why is Google, Yahoo or MSN search not good enough for searching biological database? • Discuss centralized and federated approach to data integration in the context of this case study. • Submit a softcopy of the document in the digital drop box. How to do this? Read Chapter 5, read it again. The answers can be formed from the information provided there and from your experience with relational database systems.

  20. Summary • Semantic web technologies provide an attractive technological informatics foundation for enabling the Bench to Bedside Vision. • Many areas of biomedical research including drug discovery, systems biology, personalized medicine rely heavily on integrating and interpreting heterogeneous data set. • This is part of ongoing work in the framework of the work being performed in the Healthcare and Life Sciences Interest Group of W3C.

More Related