A Design to Integrate Heterogeneous Microarray Databases

1
A Design to Integrate Heterogeneous Microarray
Databases

• Abhishek Dabral
• Jonathan Gavenda

2
Background

• What are Microarrays?
• Generally speaking DNA expression chips
• So what is the problem?
• 1000s of genes/chip 1000s of chips vast
  amount of information
• Heterogeneous databases

3
Our goal

• is the creation of anidealized system that
  actively identifies data sources of interest,
  automatically overcomes syntactic and semantic
  heterogeneities wherever it discovers them, and
  provides transparent declarative, optimized query
  access over all sources.
• Specifically targeted for microarray domains
• Benefits cost and time-efficient

4
First Step Standardization

• Microarray Gene Expression Data (MGED) Society
• Minimum Information About a Microarray Experiment
  (MIAME)
• Microarray and Gene Expression (MAGE) group

5
Methodology

• Step 1 The schemas from the data sources are to
  be extracted.
• Step 2 Tokenization step - the table name along
  with the attribute to be the token for the
  schema. Carried out for every table in each of
  the schemas.
• Step 3 An ontology is constructed from these
  schemas.
• Step 4 The object classes from Step 2 are
  clustered into class clusters

Schema
6
Methodology

• Step 5 The clusters obtained from Step 4 are
  treated as base sets and their ontologies are
  traced and the common features are examined to
  arrive at an ontology base set per cluster.
• Step 6 The ontologies in the ontology base set
  from Step 5 are integrated to obtain ontology
  clusters. The result of this step is a cluster
  of related metadata terms grouped together.
• Step 7 The ontology clusters are then named
  accordingly and recorded in the metadata updater
  to be used in the next iteration.

7
Architecture

• Semantically Enhanced Enterprise Directory
  Services (SEEDS)

Specific to the microarray domain, the
preliminary goal of microSEEDS architecture is to
minimize semantic heterogeneity through proactive
promotion of semantic homogeneity.
8
Selected References

• Microarray Gene Expression Data Society (MGED),
  http//www.mged.orgJagadish and Olken, 2003
   H. V. Jagadish and F. Olken, Database
  Managementfor Life Science Research Summary
  Report of the Workshop on Data Management
  forMolecular and Cell Biology at the National
  Library of Medicine, Bethesda,Maryland, February
  2-3, 2003, OMICS A Journal of Integrative
  Biology, 7(1),2003.Stekel, 2003 D. Stekel,
  Microarray Bioinformatics, Cambridge University
  Press,2003.Kohonen  et al., 2000 T. Kohonen,
  S. Kaski, K. Lagus, J. Salojärvi, J.Honkela, V.
  Paatero and A. Saarela, Self-Organizing Maps of
  Massive DocumentCollections, Proceedings
  International Joint Conference Neural Networks,
  IEEE,Piscataway, NJ, USA, 23-9, 2000.Li et
  al.,2004 Lei Li, Vijay Vaishnavi, and Art
  Vandenberg. "An Architecturefor Semantic
  Facilitation and Reuse of Directory Metadata"
  Proc. 2004International Conference on
  Information and Knowledge Engineering  (IKE'04
  June21-24, 2004, Las Vegas, Nevada, USA), to
  appear.Liang, J., Vaishnavi, V., and
  Vandenberg, A. Clustering of LDAP
  DirectorySchemas to Facilitate Information
  Resources Interoperability AcrossOrganizations.
  IEEE Transactions on Systems, Man, and
  Cybernetics, Part A (toappear). Liu, Y.,
  Ciliax, B. J., Borges, K., Dasigi, V., Ram, A.,
  Navathe, S., andDingledine, R. Comparison of
  Two Schemes for Automatic Keyword Extraction
  fromMEDLINE for Functional Gene Clustering.
   IEEE Conf. on Computational SystemsBioinformatic
  s (CSB2004), August 2004.Vandenberg, A.,
  Liang, J., Bolet, V., Kou, H., Vaishnavi, V., and
  Kuechler, D.Research Prototype Semantic
  Facilitator TM SM for LDAP Directory
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  on Information Technologies and Systems,2002.