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
S. F. Molaeezadeh-31 may 2008
Gene expression modeling through positive Boolean functions 2
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
S. F. Molaeezadeh-31 may 2008
Gene expression modeling through positive Boolean functions 3
Biological concepts
S. F. Molaeezadeh-31 may 2008
Gene expression modeling through positive Boolean functions 4
Microarray Technology
S. F. Molaeezadeh-31 may 2008
Gene expression modeling through positive Boolean functions 5
Gene Expression Data
S. F. Molaeezadeh-31 may 2008
Gene expression modeling through positive Boolean functions 6
Biological characteristics of gene expression data
Expression Profiles a collection of gene expression signatures
Expression signatures a cluster of coordinately expressed genes
S. F. Molaeezadeh-31 may 2008
Gene expression modeling through positive Boolean functions 7
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
S. F. Molaeezadeh-31 may 2008
Gene expression modeling through positive Boolean functions 8
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
S. F. Molaeezadeh-31 may 2008
Gene expression modeling through positive Boolean functions 9
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.
S. F. Molaeezadeh-31 may 2008
Gene expression modeling through positive Boolean functions 10
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.
S. F. Molaeezadeh-31 may 2008
Gene expression modeling through positive Boolean functions 11
The Mathematical Model
S. F. Molaeezadeh-31 may 2008
Gene expression modeling through positive Boolean functions 12
a Boolean function defined on binary strings in
S. F. Molaeezadeh-31 may 2008
Gene expression modeling through positive Boolean functions 13
cardinality
S. F. Molaeezadeh-31 may 2008
Gene expression modeling through positive Boolean functions 16
An alternative way of representing a positive Boolean function
Definition 1.
S. F. Molaeezadeh-31 may 2008
Gene expression modeling through positive Boolean functions 17
Definition 2.
S. F. Molaeezadeh-31 may 2008
Gene expression modeling through positive Boolean functions 18
For example in slide 15
S. F. Molaeezadeh-31 may 2008
Gene expression modeling through positive Boolean functions 19
Other example
S. F. Molaeezadeh-31 may 2008
Gene expression modeling through positive Boolean functions 21
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
S. F. Molaeezadeh-31 may 2008
Gene expression modeling through positive Boolean functions 23
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.