Courses
Spring 2025- Comp683: Computational Biology. [Course Page]. Fulfills the ‘Applications’ area for CS students.
Spring 2024- Comp683: Computational Biology. [Syllabus + course description], [Course Page]. Fulfills the ‘Applications’ area for CS students.
Previously was Comp790. Please contact me by email (natalies@cs.unc.edu) if you have any questions. There are technically prereqs because I was required to specify some, but if you think you will be fine with the course content, I will be happy to waive the prereqs.
Spring 2023- Comp790-166: Computational Biology [Syllabus], [Course Page]. Fulfills the ‘Applications’ area for CS students.
Course Description : Modern, high-throughput assays allow us to efficiently profile a variety biological processes to gain a systems-level understanding of health and disease. Recent technologies and experimental assays generate an abundance of detailed information that needs to be extracted, summarized, and interpreted. In this course we will discuss the methodology used to extract signal from (e.g. process, engineer features from, combine, etc.) data generated by some of the most cutting-edge experimental paradigms, such as single-cell assays and imaging. We will go into detail about the methods and theory underlying bioinformatics algorithms, originating from numerical linear algebra, graph-signal processing, and machine learning. While computational biology is a very broad field, we will focus here on applications in single-cell biology (CyTOF, single-cell RNA sequencing), multiomics/multi-modal analysis, systems immunology, and benchmarking. For each class of algorithms introduced for some task on biological data, we will also go over necessary theory and mathematical intuition. The course covers the foundations for biomedical data science and does not assume any biological knowledge.
Spring 2022 - Comp790-166: Computational Biology [Course Page]. Fulfills the ‘Applications’ area for computer science students.
Course Description : Modern, high-throughput assays allow us to efficiently profile a variety biological processes to gain a systems-level understanding of health and disease. Recent technologies and experimental assays generate an abundance of detailed information that needs to be extracted, summarized, and interpreted. In this course we will discuss the methodology used to extract signal from (e.g. process, engineer features from, combine, etc.) data generated by some of the most cutting-edge experimental paradigms, such as single-cell assays and imaging. We will go into detail about the methods and theory underlying bioinformatics algorithms, originating from numerical linear algebra, graph-signal processing, and machine learning. While computational biology is a very broad field, we will focus here on applications in single-cell biology (CyTOF, single-cell RNA sequencing), multiomics/multi-modal analysis, systems immunology, and benchmarking. For each class of algorithms introduced for some task on biological data, we will also go over necessary theory and mathematical intuition. The course covers the foundations for biomedical data science and does not assume any biological knowledge.
Spring 2021 — Comp790-166 : Computational Biology [Course Page]
This course taught in the department of computer science is a special topics course in computational biology. We focus on primarily on single-cell bioinformatics and specifically on topics, such as, automated cell-population discovery, imputation, predicting phenotypes from heterogeneity, and visualization. Other major themes include graph algorithms and multi-modal data integration in biology. We introduce relevant theory for each algorithm and problem we discuss, and therefore spend time on linear algebra, graphs, and graph signal processing.