• Introduction to Genetic Diversity
• 72 min
  1. The sources of genomic diversity

  • Dr. George Nelson
• 27 min
  2. Bioinformatics tools in conservation biology

  • Dr. Mahmood Shivji
  • Mr. Vincent Richards
• Populations, Samples and Biological Databases
• 48 min
  3. Specimen banking: saving for the future

  • Ms. Elaine Gunter
• Bioinformatics and Genome Analysis
• 44 min
  4. Accessing and using ENCODE data

  • Prof. Peggy Farnham
• 47 min
  5. Information content in sequences and its relation to protein structure prediction

  • Dr. Rosemarie Swanson
• 40 min
  6. Pattern discovery in bioinformatics

  • Prof. Giri Narasimhan
• Human Disease
• 38 min
  7. Genes in common diseases

  • Prof. Jennie Lou
• 42 min
  8. The impact of genomic architecture and diversity on infectious diseases

  • Prof. Vincent Chow
  • Dr. Meena Sakharkar
• 29 min
  9. The predominant clonal evolution (PCE) concept of microbial pathogens and its practical usefulness

  • Dr. Michel Tibayrenc
• Archived Lectures *These may not cover the latest advances in the field
• 29 min
  10. Unity and diversity in microbial genomes

  • Dr. Kishore Sakharkar
• 38 min
  11. Analysis of protein-protein interaction, transcriptional regulation and metabolic networks

  • Dr. Andreas Wagner
• 39 min
  12. Exploring and predicting phenotype and function in cancer biology: working in high dimensional data spaces

  • Prof. Robert Clarke
• 59 min
  13. Widespread structural variations in the human genome

  • Dr. Charles Lee
• 39 min
  14. Human genome variation

  • Dr. Kelly Frazer
• 36 min
  15. Human genetic variation and therapeutic development

  • Dr. Sally John
  • Dr. Nick Davies
• 62 min
  16. Evolving genomic approaches to the study of complex cardiovascular diseases in human populations: genetic and genomic epidemiology

  • Dr. Christopher O'Donnell
• 42 min
  17. Population genetics studies in Africa: curiosity and challenges

  • Prof. Himla Soodyall
• 37 min
  18. Sample collection for human genetics

  • Dr. Michael Dean
• 40 min
  19. Diagnosis of early relapse in ovarian cancer using serum proteomic profiling

  • Dr. Jean Gao

Printable Handouts

PDF

Navigable Slide Index

1. Introduction
2. Outline
3. Mass spectrometry (MS) in proteomic research
4. SELDI-TOF
5. Principle for SELDI-TOF MS
6. Serum proteomic profiling
7. A typical proteomic pattern discovery system
8. Platinum-sensitive/resistant ovarian cancer
9. Recurrent ovarian cancer
10. Platinum resistant and sensitive
11. Background algorithms
12. SVM (support vector machine)
13. SVM algorithm
14. SVM-RFE (recursive feature elimination)
15. Markov Blanket (1)
16. Markov Blanket (2)
17. Proposed algorithm: SVM-MB/RFE
18. SVM-MB/RFE block diagram
19. Preprocessing: t-test
20. Preprocessing: binning
21. Scoring function: SVM-RFE/MB
22. Experimental results
23. Results: sample collection
24. Results: peaks
25. Results: accuracy after SVM-RFE/MB
26. Results: sensitivity
27. Results: specificity
28. Results: frequency and t-test (1)
29. Results: frequency and t-test (2)
30. Results: comparison with different MB size k
31. Results: accuracy with different k
32. Outline - conclusions
33. Conclusions
34. Contributors
35. Thank You!

Topics Covered

• Surface-enhanced laser desorption/ionization time-of-flight (SELDI-TOF) mass spectrometry data
• Use of Biomarkers to identify diverse early disease detection
• Ovarian cancer relapse diagnosis
• SVM-MB/RFE: Support Vector Machine- Markov Blanket/Recursive Feature Elimination to identify biomarkers for predicting the early recurrence of ovarian cancer by analyzing SELDI-TOF mass spectrometry data

Links

Series:

• Using Bioinformatics in the Exploration of Genetic Diversity

Categories:

• Cancer
• Methods
• Reproduction & Development

Therapeutic Areas:

• Gynaecology & Obstetrics
• Oncology

Talk Citation

Publication History

• Published on October 1, 2007
• Archived on October 28, 2018

Financial Disclosures

• Dr. Jean Gao has not informed HSTalks of any commercial/financial relationship that it is appropriate to disclose.