S. F. Molaeezadeh-31 may 2008Gene 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 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 2 Outlines Biological concepts Microarray Technology Gene Expression Data Biological characteristics of gene expression data Modeling Objects Modeling Issues The Mathematical Model An application to the evaluation of gene selection methods Conclusions Biological concepts Microarray Technology Gene Expression Data Biological characteristics of gene expression data Modeling Objects Modeling Issues The Mathematical Model An application to the evaluation of gene selection methods Conclusions

S. F. Molaeezadeh-31 may 2008Gene expression modeling through positive Boolean functions 3 Biological concepts

S. F. Molaeezadeh-31 may 2008Gene expression modeling through positive Boolean functions 4 Microarray Technology

S. F. Molaeezadeh-31 may 2008Gene expression modeling through positive Boolean functions 5 Gene Expression Data

S. F. Molaeezadeh-31 may 2008Gene 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 2008Gene 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 2008Gene 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

S. F. Molaeezadeh-31 may 2008Gene 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 2008Gene 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 2008Gene expression modeling through positive Boolean functions 11 The Mathematical Model

S. F. Molaeezadeh-31 may 2008Gene expression modeling through positive Boolean functions 12 a Boolean function defined on binary strings in

S. F. Molaeezadeh-31 may 2008Gene expression modeling through positive Boolean functions 13 cardinality

S. F. Molaeezadeh-31 may 2008Gene expression modeling through positive Boolean functions 14

S. F. Molaeezadeh-31 may 2008Gene expression modeling through positive Boolean functions 15

S. F. Molaeezadeh-31 may 2008Gene expression modeling through positive Boolean functions 16 An alternative way of representing a positive Boolean function Definition 1.

S. F. Molaeezadeh-31 may 2008Gene expression modeling through positive Boolean functions 17 Definition 2.

S. F. Molaeezadeh-31 may 2008Gene expression modeling through positive Boolean functions 18 For example in slide 15

S. F. Molaeezadeh-31 may 2008Gene expression modeling through positive Boolean functions 19 Other example

S. F. Molaeezadeh-31 may 2008Gene expression modeling through positive Boolean functions 20

S. F. Molaeezadeh-31 may 2008Gene 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 2008Gene expression modeling through positive Boolean functions 22

S. F. Molaeezadeh-31 may 2008Gene 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.

S. F. Molaeezadeh-31 may 2008Gene expression modeling through positive Boolean functions 24 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