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Printable Handouts
Navigable Slide Index
- Introduction
- Cancer
- Information flow in cells
- Some basic assumptions
- How genes affect phenotype?
- Global expression analysis
- Hybridization: the basic principle
- Glass slide microarrays
- Competitive hybridization
- GeneChip and CodeLink microarrays
- Comparative hybridization
- Biological questions
- Data properties: supervised or unsupervised
- Separability
- Study goals (1)
- Study goals (2)
- Characteristics of microarray data
- Structure of microarray data (1)
- Structure of microarray data (2)
- Curse of dimensionality
- Concentration of measure
- Blessings of smoothness
- Multimodality (1)
- Multimodality (2)
- An example study
- Breast cancer questions
- Supervising data
- Analysis approach
- Reducing dimensionality (1)
- Reducing dimensionality (2)
- Clustering and gene selection
- Validation
- Summary
- 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
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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 October 13, 2024, from https://doi.org/10.69645/JGKB7098.Export Citation (RIS)
Publication History
Financial Disclosures
- Prof. Robert Clarke has not informed HSTalks of any commercial/financial relationship that it is appropriate to disclose.
Exploring and predicting phenotype and function in cancer biology: working in high dimensional data spaces
A selection of talks on Oncology
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