1. Reconstructing Gene Networks Presented by Andrew Darling Based on article  “Research Towards Reconstruction of Gene Networks from Expression Data by Supervised Learning” - Soinov, Krestyaninova, Brazma

2. Outline Why study another microarray algorithm? Background info Methods Results Discussion Conclusion

3. Why study another microarray algorithm? Study of microarray data continues  Still unclear on what the data means  Still unclear on how the genome works Confirm existing knowledge about gene networks using existing datasets Proof of concept in a new algorithm using existing knowledge and datasets This algorithm actually explains its reasoning

4. Background information What is a gene network? What is supervised learning? What are decision trees / classifiers? Why use classifiers?

5. What is a gene network? A model of a genes affecting other genes  What other genes affect a given gene  How other genes affect a given gene Positive, negative, complicated Several model types – graphs, nodes, edges  Boolean ( on – off )  Bayesian network ( conditional probability )  Differential equations ( derivatives, integrals )

6. Gene network - example

7. What is supervised learning? The paper was unclear on the subject  Perhaps a reference to the type of algorithm used  It may have involved human interaction with the software  Possibly, the software produced the classifiers in the form of a decision tree, then users interpreted the output into classification rules

8. What are decision trees / classifiers? Acyclic directed graph - tree  Each graph explains what other genes affect a specific gene  Inner nodes are gene products of other genes  Edges are thresholds of concentration of the gene products of the other genes – rules of the tree  Leaf nodes are effects on transcription of the specific gene Each graph is a classifier for a specific gene

9. Classifiers – model of gene networks Expression of gene is function of transcription Transcription of gene is in discrete states  Expressed more than average  Expressed less than average Transcription state affected by amount of other gene products (expression of other genes) Use yeast cell cycle data to test algorithm and previous knowledge to judge accuracy

10. Why use classifiers? The products affecting a specific gene are listed in the tree Allows for continuous values for concentrations Each additional dataset refines the decision information Decision trees are easy to read and interpret

11. Classifier - example

12. Methods Use induction algorithm to generate decision trees  Program called C4.5 Apply program three ways  Regulation of target gene as a function of other genes at same time (simultaneous)  Regulation of target gene as a function of other genes at previous times (time delay)  Regulation as a function of change of other genes (changes)

13. Results - given These genes and yeast datasets  Spellman, Cho, … Cdc28 Alpha-factor

14. Results – produced this

15. Results – with this accuracy

16. Discussion Some concern about the accuracy between 70% and 94% on systems with known interactions Does that imply that the microarray data is wrong or the algorithm is flawed?

17. Conclusions Decision trees and classifiers seem a better way to explain gene expression This paper did not do a good job of explaining how to make / use them Reference to the algorithm itself was almost specious