BIOCARTA KEGG BIOCYC. OMIM Mammalian Phenotype Others. Pathways. Disease. Discovering Disease Associations using a Biomedical Semantic Web: Integration and Ranking. Ranga Chandra Gudivada 1,2 , Xiaoyan A. Qu 1,2, Anil G Jegga 2,3,4 , Eric K. Neumann 5 , Bruce J Aronow 1,2,3,4
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Discovering Disease Associations using a Biomedical Semantic Web: Integration and Ranking
Ranga Chandra Gudivada1,2, Xiaoyan A. Qu 1,2, Anil G Jegga2,3,4, Eric K. Neumann5 , Bruce J Aronow1,2,3,4
Departments of Biomedical Engineering1 and Pediatrics2, University of Cincinnati, Center for Computational Medicine3 and Division of Biomedical Informatics4,
Cincinnati Children’s Hospital Medical Center, Cincinnati OH-45229, USA and Teranode Corporation5, Seattle, WA 98104
Case Study-Prioritizing Modifier Genes, Pathways and Biological Processes for CARDIOMYOPATHY, DILATED
Data Integration- RDF MODEL
One of the principal goals of biomedical research is to elucidate the complex network of gene interactions underlying common human diseases. Although integrative genomics based approaches have been shown to be successful in understanding the underlying pathways and biological processes in normal and disease states, most of the current biomedical knowledge is spread across different databases in different formats. Semantic Web principals, standards and technologies provide an ideal platform to integrate such heterogeneous information and bring forth implicit relations hitherto embedded in these large integrated biomedical and genomic datasets. Semantic Web query languages such as SPARQL can be effectively used to mine the biological entities underlying complex diseases through richer and complex queries on this integrated data. However, the end results are frequently large and unmanageable. Thus, there is a great need to develop techniques to rank resources on the Semantic Web which can later be used to retrieve and rank the results and prevent the information overload. Such ranking can be used to prioritize the discovered disease–gene, disease–pathway or disease–processes novel relationships. We implemented an existing semantic web based knowledge mining technique which not only discovers underlying genes, processes and pathways of diseases but also determines the importance of the resources to rank the results of a search while determining the semantic associations.
Gene / Protein
SELECT DISTINCT ?pathway
?pathway rdf:type CCHMC:Pathway .
?resource ?PROPERTY ?pathway .
Ranking on Semantic Web
KleinBerg Algorithm (1)
Modifier Genes (16)
QUERY RESULTWITH PRIORITIZATION
Pointed by good hubs its authoritative score increases
Points to many authoritative sites, increases the hub scores
High Hub score
High Authoritative score
We have shown that related yet heterogeneous information can be integrated using RDF-OWL and that this approach can support mechanistic analyses of diseases. Specifically, we have uncovered additional genes and pathways that could play a role in the onset and treatment of Cardiomyopathy. We intend to expand our analyses into additional modalities such as anatomy, cellular type, and symptoms/ phenotypes.
Data integration: biological feature complexity is deep, heterogeneous, and extensive.
Data complexity poses a formidable challenge to efforts to integrate, formally model, and simulate biological systems behaviors
Likelihood Ranking requires mining and prioritization of entities and events that function in the context of biological networks
Extending ‘KleinBerg Algorithm’(2) for Semantic Web
A single gene participating in multiple biological pathways is considered more sensitive to perturbation than a single pathway having a large number of nodes (Different weights for non - symmetric properties); corollary :
Benefits of Semantic Web
Subjectivity weight > objectivity weight
1.Kleinberg, J. M. 1999. Authoritative sources in a hyperlinked environment. J. ACM 46, 5 (Sep. 1999)
2 Bhuvan Bamba, Sougata Mukherjea: Utilizing Resource Importance for Ranking Semantic Web Query Results. SWDB 2004: 185-198
Biological Processes (27)
GeneA interacting with various genes has
equal significance as GeneB interacting with
various genes (Equal weights for symmetric
Subjectivity weight = objectivity weight