BME 6770: Genomic Signal Processing
- For postdoctoral, graduate, and advanced undergraduate students in Engineering, Sciences, and Medicine, and professionals in industry.
- Spring 2020, Mondays and Wednesdays 11:50am–1:10pm, LCB 115.
Prerequisites: Some experience programming and instructor approval.
100% grade = 30% labs, 30% presentation, 30% class project, 10% class participation; class attendance is required.
Concepts in artificial intelligence, data science, and machine learning and their applications in the integration and comparison of different types of high-throughput omic data acquired by different technologies and from different studies toward discovery, verification, and validation of biomedical principles.
- Technologies, for for high-throughput acquisition of different types of molecular biological data, e.g., omics, imaging, and patient clinical information.
- Databases, from the Cancer Genome Atlas (TCGA) at the Genomic Data Commons (GDC) to the Cancer Image Archive (TCIA).
- Mathematical frameworks, from the singular value decomposition (SVD) and principal component analysis (PCA) to multi-matrix 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.
- Proving mathematical theorems and programming symbolic computations.
- Designing algorithms and programming numerical computations.
- Working with databases and modeling biomedical data.
- 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.
- Spring 2020 Calendar
- Health, Wellness, and Counseling
- Student Code
Technologies and databases: On the Utah origin of the human genome project
The Alta Summit, US Department of Energy.
Mathematical frameworks: The SVD
Numerical Linear Algebra, Trefethen and Bau, III (1997).
Code the SVD of synthetic data and its visualization. Test and debug your code.
Happy Dr. Martin Luther King, Jr. Day!
"Injustice anywhere is a threat to justice everywhere."
In-Class Project 1: Derive the hypergeometric distribution from first combinatorics principles.
In-Class Project 2: Download two interrelated omic profiles from TCGA via GDC, e.g., (i) RNA sequencing profiles of the tumors of two different patients of the same type of cancer or two different types of cancer, (ii) gene expression profiles from the tumor of one cancer patient measured by two different biotechnologies, or (iii) DNA methylation and protein expression profiles of the tumor of one cancer patient. How would you interpret these profiles?
January 30, Thursday, 10:00–11:00pm, in lieu of any one Lab:
Mathematical properties of the SVD
In-Class Work on Lab 2:
Compute and visualize the SVD 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 21, Friday, 8:00–11:00am, Utah State Capitol Rotunda, 350 North State Street, Salt Lake City, in lieu of any one Lab:
Mathematics of the pseudoinverse
Mathematics of the pseudoinverse:
March 5, Tuesday, 10:00am–12:00pm, WEB 3780:
March 11 and 13:
March 21, Thursday, 10:00am–12:00pm, WEB 3780:
"State of the Project" Presentations
"State of the Project" Presentations
Paper 17: Generalized Singular Value Decomposition for Comparative Analysis of Genome-Scale Expression Datasets of Two Different Organisms, Alter et al., Proc Natl Acad Sci USA (2003).
Paper 18: Combining Transcriptional Datasets Using the Generalized Singular Value Decomposition, by Schreiber et al., BMC Bioinformatics (2008).
Paper 19: Exploring Metabolic Pathway Disruption in the Subchronic Phencyclidine Model of Schizophrenia with the Generalized Singular Value Decomposition, by Xiao et al., BMC Syst Biol (2011).
April 10 and 15:
In-Class Work on Lab 3:
Select two or more datasets, and explain how you might compare or integrate these data by using, e.g., pseudoinverse projection or GSVD. Explain also and the mathematical variables, and if possible also the mathematical operations, operations of your integrative or comparative model might mean biologically.
April 16, Tuesday, 10:00am–12:00pm, WEB 3780:
Class Project "Data Clinic"
Happy Summer Break!