BME 6770: Genomic Signal Processing
- For postdoctoral, graduate, and advanced undergraduate students in Engineering, Sciences, and Medicine, and professionals in industry.
- Spring 2024, Mondays and Wednesdays 11:50am–1:10pm, LCB 115 and Zoom; office hours by request or Wednesdays 11:00am, WEB 3803.
Prerequisites: Some experience programming and instructor approval.
100% grade = 30% labs, 30% presentation, 30% class project, 10% class participation; late assignments are not accepted; class attendance is required.
May be pivotal for your career:
Topics:
We will cover concepts in artificial intelligence, data science, and machine learning and their applications in the integration and comparison of different types of high-throughput omic and other data acquired by different technologies and from different studies toward discovery, verification, and validation of biomedical principles.
- Technologies, such as whole genome-sequencing, for high-throughput acquisition of different types of molecular biological data and patient clinical information.
- Databases, such as the Cancer Genome Atlas (TCGA) at the Genomic Data Commons (GDC).
- Algorithms, from the singular value decomposition (SVD) and principal component analysis (PCA) to multi-tensor decompositions, neural networks, and deep learning.
- Applications toward better understanding of biology and practice of medicine, e.g., personalized cancer diagnostics, prognostics, and therapeutics.
Skills:
- Proving mathematical theorems and programming symbolic computations.
- Designing algorithms and programming numerical computations.
- Working with databases and modeling biomedical data.
Activities:
- In-class presentations of scientific journal articles and patents.
- Participation in guest lectures and seminars on campus and discussions of conference reports.
- End-of-class celebration.
Readings on the SVD and deep learning:
- Syllabus
- COVID-19
- Spring 2024 Calendar
- Safety
- Health, Wellness, and Counseling
- Student Code
January 8:
January 10:
January 15:
Happy Martin Luther King Jr. Day!
January 17:
Numerical Linear Algebra, Trefethen and Bau, III (1997).
Notebook 1: Computation and Visualization of the SVD
Lab 1:
Code the SVD or the pseudoinverse projection of synthetic data and its visualization. Test and debug your code.
January 22:
Composition and decomposition of synthetic data:
Notebook 2: The SVD of Synthetic Data
January 24:
2024 Precision Medicine World Conference
January 29:
Mathematics of the pseudoinverse projection:
January 31:
More examples of the SVD of measured data:
Paper 3: Singular Value Decomposition for Genome-Wide Expression Data Processing and Modeling, Alter et al., Proceedings of the National Academy of Sciences (PNAS) USA (2000).
Patent 1: Method for Node Ranking in a Linked Database, Page, United States Patent (2001).
Paper 4: A Rapid Genome-Scale Response of the Transcriptional Oscillator to Perturbation Reveals a Period-Doubling Path to Phenotypic Change, Li and Klevecz, Proceedings of the National Academy of Sciences (PNAS) USA (2006).
Paper 5: Coordinated Metabolic Transitions During Drosophila Embryogenesis and the Onset of Aerobic Glycolysis, Tennessen, Bertagnolli et al., G3: Genes, Genomes, Genetics (2014).
February 5:
Computation of the pseudoinverse projection:
Notebook 3: The Pseudoinverse Projection of Measured Data
February 7:
February 12:
February 14:
February 19:
February 26:
February 28:
In-Class Work on Lab 2:
Compute and visualize the SVD or the pseudoinverse projection of your data. Interpret your data based upon its SVD. Use at least two different approaches each for preprocessing and sorting your data and for assessing the statistical significance of your interpretation.
Notebook 4: The Hypergeometric Probability Distribution and P-Value
February 29:
Matrix Computations, Golub and Van Loan (1996).
March 4:
March 6:
March 11:
March 13:
From the SVD to PCA:
March 15, 11:45am–1:15pm, SMBB 2650:
Bioengineering Elevated Lecture Celebrating 50 Years of Biomedical Engineering at the University of Utah
March 18:
March 20:
Mathematical variations on the SVD and PCA for blind source separation (BSS):
March 25:
Mathematics of the generalized SVD (GSVD):
March 27:
Computation of the GSVD:
Notebook 5: The GSVD of Synthetic Data
April 1:
April 3:
April 8:
April 10:
April 15:
The Kaplan-Meier survival analysis and the log-rank P-value
April 17:
"State of the project" presentations
April 22:
End-of-class celebration!
"State of the project" presentations
Happy Summer Break!
