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Heterogeneous Association Rules
Mining
Badr Al-Daihani
School of Computer
Science
Cardiff University
Edinburgh,UK
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Overview
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Motivation
Challenges of Bioinformatics Databases Management
Approaches to integration of bioinformatics databases
Association rule mining
Hypothesis
Basic concepts
Material and methods
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Motivation
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Very large heterogeneous databases.
Need to link.
Integration.
Complex relation.
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Challenges of Bioinformatics
Databases Management
Bioinformatics Databases format:
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Flat files: GenBank, EMBL, DDBJ, PDB.
Relational databases: HGMD, MGMD
Object-oriented database: AceDB.
XML databases: PIR, SwissProt, InterPro.
Characteristics:
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The Diversity/variety of data.
The representational heterogeneity.
Autonomous and web-based sources.
Varied interface and query capabilities
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Approaches to integration of
bioinformatics databases
Multiple models of data integration:
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Federation
Warehousing
Mediations
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Federation
Provides access to distributed data while preserving database autonomy
examples:
K2/BioKleisli
Entrez
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Warehousing
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import data from remote sources and copy to local server
Example:
GUS (Genome Unified Schema)
Sequence Retrieval System (SRS)
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Mediations
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stores no data on its own rather it provides a virtual view of the
integrated sources
Examples:
Transparent Access to Multiple Bioinformatics Information Source
(TAMBIS)
Knowledge-based Integration of Neuroscience Data (KIND)
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Hypothesis:
It is possible to mine diverse databases to recover datasets related to
a disease, associated gene mutations and mutagens which aid
scientists understanding of their cause.
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Association Rules
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Association Rules –interesting association relationship among huge
amounts of transactions
An association rule is an expression of the form X => Y, where X and Y
are sets of items
Goal of AA – To find all association rules that satisfy user-specified
minimum support and minimum confidence threshold
Examples.
– Rule form: “Body ead [support, confidence]”.
– buys(x, “diapers”)  buys(x, “beers”) [0.5%, 60%]
– major(x, “CS”) ^ takes(x, “DB”) grade(x, “A”) [1%, 75%]
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Association Rules
Applications:
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Basket data analysis
Genomic Data
Cross-marketing
Catalog design
sale campaign analysis
Web Personalization
clustering, classification, etc.
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Basic Concepts
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The discovery of interesting association relationships among huge
amount of gene mutation can help in determining the cause of
mutation in tumours and diseases.
Gene is a segment of a DNA molecule that contains all the information
required for synthesis of a product.
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Gene mutation is any change in the DNA sequence of a gene.
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Types of mutations: Insertion, Deletion, Insertion/Deletion, Complex,
and Multiple Substitution
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Material and Methods
HGMD database
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The Human Gene Mutation Database (HGMD) runs by University of
Wales College of Medicine.
Known (published) gene lesions responsible for human inherited
disease.
Provides information about practical diagnoses.
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Material and Methods
MGMD database
 The Mammalian Gene Mutation Database (MGMD).
 Runs by Centre of Molecular Genetics and Toxicology, University of
Wales Swansea.
 profiles of known (published) mutagen-induced gene mutations.
 Stores the mutation spectra information.
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It has 39134 records.
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Material and Methods
Sets of items whose elements tend to be in both databases will be
retrieved to discover the interesting association rules among genes,
mutations, mutagens and diseases.
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Material and Methods
Graphical User Interface (GUI)
Mining tools
Query interpreter
Wrapper
DBn
Wrapper
MGMD
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Wrapper
HGMD
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References
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[1] Hernandez T. and Kambhampati S. (2004) Integration of Biological Sources: Current
Systems and Challenges Ahead, Proc. of the ACM SIGMOD Conference.
[2] C. Goble et al. (2001) Transparent access to multiple bioinformatics information
sources. IBM Systems Journal, 40(2).
[3] Barbara Eckman,Zoe Lacroix and Louiqa Raschid (2001) Optimized Seamless
Integration of Biomolecular Data,IEEE, International Conference on Bioinformatics and
Biomedical Egineering,23-32. [4] Lacroix Z, Boucelma O and Essid M (2003) The Biological
Integration System. Proc. of the 5th ACM Workshop on Web Information and data
management, pp 45-49.
[5] Aldana J.,Roldán M, Navas I, Pérez A and Trelles O (2004) Integrating Biological Data
Sources and Data Analysis Tools through Mediators, Proceedings of the 2004 ACM
symposium on Applied computing.
[6]. Agrawal, R.-Imielinski, T.-Swami, A. (1993) Mining Association Rules Between Sets of
Items in Large Databases. Proc. ACM SIGMOD:207-216.
[7] P.D. Lewis, J.S. Harvey, E.M. Waters, and J.M. Parry
(2000) The Mammalian Gene Mutation Database, Mutagenesis, 15(5): 411- 414.
[8] Krawczak M, Ball EV, Fenton I, Stenson PD, Abeysinghe S, Thomas N, Cooper DN
(2000): Human Gene Mutation Database - a biomedical information and research resource.
Human Mutation 15(1):45-51.
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