S. F. Molaeezadeh-31 may 2008 Gene expression modeling through positive Boolean functions 1 Seminar Title: Gene expression modeling through positive Boolean functions By seyyedeh Fatemeh Molaeezadeh Supervisor: Dr. farzad Towhidkhah 31 may 2008 In the Name of Allah
S. F. Molaeezadeh-31 may 2008Gene expression modeling through positive Boolean functions 1 Seminar Title: Gene expression modeling through positive Boolean.
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S. F. Molaeezadeh-31 may 2008
Gene expression modeling through positive Boolean functions 1
Seminar Title:
Gene expression modeling through positive Boolean functions
Seminar Title:
Gene expression modeling through positive Boolean functions
By seyyedeh Fatemeh MolaeezadehSupervisor: Dr. farzad Towhidkhah
31 may 2008
By seyyedeh Fatemeh MolaeezadehSupervisor: Dr. farzad Towhidkhah
31 may 2008
In the Name of AllahIn the Name of Allah
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Outlines
Biological conceptsMicroarray TechnologyGene Expression DataBiological characteristics of gene expression dataModeling ObjectsModeling IssuesThe Mathematical ModelAn application to the evaluation of gene selection methodsConclusions
Biological conceptsMicroarray TechnologyGene Expression DataBiological characteristics of gene expression dataModeling ObjectsModeling IssuesThe Mathematical ModelAn application to the evaluation of gene selection methodsConclusions
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Biological concepts
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Microarray Technology
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Gene Expression Data
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Biological characteristics of gene expression data
Expression Profiles a collection of gene expression signatures
Expression signatures a cluster of coordinately expressed genes
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Characteristics of gene expression signatures
Differential expression and co-expression
Gene expression signatures as a whole rather than single genes contain predictive information.
Genes may belong to different gene expression signatures at the same time
Expression signatures may be independent of clinical parameters
Different gene expression profiles may share signatures and may differ only for few signatures
Differential expression and co-expression
Gene expression signatures as a whole rather than single genes contain predictive information.
Genes may belong to different gene expression signatures at the same time
Expression signatures may be independent of clinical parameters
Different gene expression profiles may share signatures and may differ only for few signatures
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Modeling Objects
Evaluation of the performance of a statistic or learning methods such as gene selection and clustering
Evaluation of the performance of a statistic or learning methods such as gene selection and clustering
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Modeling Issues
1. Expression profiles may be characterized as a set of gene expression signatures
2. Expression signatures are interpreted in the literature as a set of coexpressed genes
3. the model should permit to define arbitrary signatures
4. Genes may belong to different signatures at the same time.
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The number of genes within an expression signature usually vary from few units to few hundreds.
the model should reproduce the variation of gene expression data.
Not all the genes within a signature may be expressed in all the samples.
Different expression profiles may differ only for few signatures
The model should be sufficiently flexible to allow different ways of constructing an expression profile.
The number of genes within an expression signature usually vary from few units to few hundreds.
the model should reproduce the variation of gene expression data.
Not all the genes within a signature may be expressed in all the samples.
Different expression profiles may differ only for few signatures
The model should be sufficiently flexible to allow different ways of constructing an expression profile.
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The Mathematical Model
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a Boolean function defined on binary strings in
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cardinality
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An alternative way of representing a positive Boolean function
Definition 1.
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Definition 2.
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For example in slide 15
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Other example
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An application to the evaluation of gene selection methods
• Dataset:
– 100 artificial tissues, 60 belonging to the first class and 40 in the second class, with 6000 virtual genes.
• Gene selection method:
– Golub method (a simple variation of the classic t-test)
– the SVM-RFE procedure
• Evaluation method:– Intersection percent between selected gene set from above mentioned methods
and marker gene set that we produce
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Conclusions
• introduce a mathematical model based on positive Boolean functions
• take account of the specific peculiarities of gene expression
• the biological variability viewed as a sort of random source.
• Present an applicative example.
• introduce a mathematical model based on positive Boolean functions
• take account of the specific peculiarities of gene expression
• the biological variability viewed as a sort of random source.
• Present an applicative example.
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Reference
Francesca Ruffino; Marco Muselli; Giorgio Valentini. “Gene expression modeling through positive Boolean functions”, International Journal of Approximate Reasoning, Vol. 47, 2008, pp. 97–108
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