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- Introduction to Genetic Diversity
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1. The sources of genomic diversity
- Dr. George Nelson
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2. Bioinformatics tools in conservation biology
- Dr. Mahmood Shivji
- Mr. Vincent Richards
- Populations, Samples and Biological Databases
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3. Specimen banking: saving for the future
- Ms. Elaine Gunter
- Bioinformatics and Genome Analysis
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4. Accessing and using ENCODE data
- Prof. Peggy Farnham
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5. Information content in sequences and its relation to protein structure prediction
- Dr. Rosemarie Swanson
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6. Pattern discovery in bioinformatics
- Prof. Giri Narasimhan
- Human Disease
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7. Genes in common diseases
- Prof. Jennie Lou
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8. The impact of genomic architecture and diversity on infectious diseases
- Prof. Vincent Chow
- Dr. Meena Sakharkar
- Archived Lectures *These may not cover the latest advances in the field
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10. Unity and diversity in microbial genomes
- Dr. Kishore Sakharkar
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13. Widespread structural variations in the human genome
- Dr. Charles Lee
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14. Human genome variation
- Dr. Kelly Frazer
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15. Human genetic variation and therapeutic development
- Dr. Sally John
- Dr. Nick Davies
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17. Population genetics studies in Africa: curiosity and challenges
- Prof. Himla Soodyall
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18. Sample collection for human genetics
- Dr. Michael Dean
Printable Handouts
Navigable Slide Index
- Introduction
- Outline
- Mass spectrometry (MS) in proteomic research
- SELDI-TOF
- Principle for SELDI-TOF MS
- Serum proteomic profiling
- A typical proteomic pattern discovery system
- Platinum-sensitive/resistant ovarian cancer
- Recurrent ovarian cancer
- Platinum resistant and sensitive
- Background algorithms
- SVM (support vector machine)
- SVM algorithm
- SVM-RFE (recursive feature elimination)
- Markov Blanket (1)
- Markov Blanket (2)
- Proposed algorithm: SVM-MB/RFE
- SVM-MB/RFE block diagram
- Preprocessing: t-test
- Preprocessing: binning
- Scoring function: SVM-RFE/MB
- Experimental results
- Results: sample collection
- Results: peaks
- Results: accuracy after SVM-RFE/MB
- Results: sensitivity
- Results: specificity
- Results: frequency and t-test (1)
- Results: frequency and t-test (2)
- Results: comparison with different MB size k
- Results: accuracy with different k
- Outline - conclusions
- Conclusions
- Contributors
- 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:
Categories:
Therapeutic Areas:
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 March 20, 2025, from https://doi.org/10.69645/SWCG7298.Export Citation (RIS)
Publication History
Financial Disclosures
- Dr. Jean Gao has not informed HSTalks of any commercial/financial relationship that it is appropriate to disclose.
Diagnosis of early relapse in ovarian cancer using serum proteomic profiling

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