Printable Handouts

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Navigable Slide Index

1. Introduction
2. Cancer
3. Information flow in cells
4. Some basic assumptions
5. How genes affect phenotype?
6. Global expression analysis
7. Hybridization: the basic principle
8. Glass slide microarrays
9. Competitive hybridization
10. GeneChip and CodeLink microarrays
11. Comparative hybridization
12. Biological questions
13. Data properties: supervised or unsupervised
14. Separability
15. Study goals (1)
16. Study goals (2)
17. Characteristics of microarray data
18. Structure of microarray data (1)
19. Structure of microarray data (2)
20. Curse of dimensionality
21. Concentration of measure
22. Blessings of smoothness
23. Multimodality (1)
24. Multimodality (2)
25. An example study
26. Breast cancer questions
27. Supervising data
28. Analysis approach
29. Reducing dimensionality (1)
30. Reducing dimensionality (2)
31. Clustering and gene selection
32. Validation
33. Summary
34. Acknowledgments

Topics Covered

• Genes and phenotypes
• Gene expression microarrays
• Data properties
• Structure of microarray data
• Curse of dimensionality
• Concentration of measure
• Blessing of smoothness
• Multimodality
• Data analysis
• Reducing dimensionality
• Clustering and gene selection
• Validation

Links

Series:

• Using Bioinformatics in the Exploration of Genetic Diversity

Categories:

• Cancer
• Genetics & Epigenetics
• Methods

Therapeutic Areas:

• Oncology

Talk Citation

Clarke, R. (2007, October 1). Exploring and predicting phenotype and function in cancer biology: working in high dimensional data spaces [Video file]. In The Biomedical & Life Sciences Collection, Henry Stewart Talks. Retrieved November 4, 2025, from https://doi.org/10.69645/JGKB7098.
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Publication History

• Published on October 1, 2007
• Archived on May 31, 2018

Financial Disclosures

• Prof. Robert Clarke has not informed HSTalks of any commercial/financial relationship that it is appropriate to disclose.