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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

Gao, J. (2007, October 1). Diagnosis of early relapse in ovarian cancer using serum proteomic profiling [Video file]. In The Biomedical & Life Sciences Collection, Henry Stewart Talks. Retrieved December 26, 2024, from https://doi.org/10.69645/SWCG7298.
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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.