BME 6770: Genomic Signal Processing
- For postdoctoral, graduate, and advanced undergraduate students in Engineering, Sciences, and Medicine, and professionals in industry.
- Spring 2022, Mondays and Wednesdays 11:50am–1:10pm, Zoom.
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.
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 2022 Calendar
- Safety
- Health, Wellness, and Counseling
- Student Code
January 10:
January 12:
January 17:
Happy Martin Luther King Jr. Day!
January 19:
Numerical Linear Algebra, Trefethen and Bau, III (1997).
Lab 1:
Code the SVD or the pseudoinverse projection of synthetic data and its visualization. Test and debug your code.
January 24:
Composition and decomposition of synthetic data:
January 31:
February 2:
February 7:
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 9:
Mathematics of the pseudoinverse projection:
February 9, 1:20–2:00pm:
February 12:
February 14:
February 16:
February 21:
February 23:
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.
February 29:
Matrix Computations, Golub and Van Loan (1996).
March 2:
March 7:
March 9:
March 14:
March 16:
From the SVD to PCA:
March 21:
March 23:
Mathematical variations on the SVD and PCA for blind source separation (BSS):
March 28:
Mathematics of the generalized SVD (GSVD):
March 30:
April 1, 11:50am–1:10pm:
April 4:
April 6:
April 11:
April 13:
April 18:
"State of the project" presentations
April 20:
"State of the project" presentations
April 25:
End-of-class celebration!
Happy Summer Break!
