BME 6770: Genomic Signal Processing
Mention: Course may "be pivotal for … career," Amazon Science (April 6, 2022).
- 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; office hours upon request or Wednesdays 11:00am, WEB 3803.
Prerequisites: Some experience programming and instructor approval.
100% grade = 40% labs and quizzes, 30% class project, 20% presentation, 10% class participation; late assignments are not accepted; class attendance is required.
This project-oriented genomics, data science, and artificial intelligence and machine learning (AI/ML) course covers:
- Technologies: For high-throughput acquisition of molecular biological data on genomic and proteomic scales, such whole-genome sequencing and protein arrays.
- Databases and Large-Scale Datasets: Generated by national and international consortia as well as individual research groups using these technologies.
- Comparative and Integrative Modeling: Using concepts from signal processing andAI/ML, from the singular value decomposition (SVD) and principal component analysis (PCA), to the pseudoinverse projection and linear regression, and to large language models.
- Biological and Medical Predictions: Made by these comparative and integrative models, their experimental tests and their applications toward better understanding of basic biology as well as improved medical diagnosis, treatment, and drug design.
Objective/Outcome:
The design and realization of a data science project by each student.
Skills:
- Proving mathematical theorems and coding symbolic computations.
- Designing algorithms and programming numerical computations.
- Working with biomedical databases.
- Modeling biomedical data.
Activities:
- In-class presentations of, e.g., scientific journal articles and patents.
- Participation in guest lectures and seminars on campus and discussions of professional conference reports.
- End-of-class celebration and discussion of future directions.
Readings on the SVD and deep learning:
- The syllabus is subject to change at the discretion of the instructor.
- Spring 2024 Calendar
- University Policies
- Health, Wellness, and Counseling
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!
