Systems Biology: Practical Data Analysis

CHEM M291 - Systems Biology: Practical Data Analysis

<p>(Same as Chemistry M291.) Lecture, three hours. Practical training in the analysis of large-scale biological datasets using Python. Students work with real-world data from single-cell RNA-seq, spatial transcriptomics, phospho-proteomics, and high-content imaging studies. Emphasis on hands-on coding, statistical and machine learning modeling, and clear data interpretation and visualization. A key component is the responsible use of artificial intelligence coding assistants. Students learn how to use tools like ChatGPT and Copilot to scaffold code, debug, and accelerate their analysis, while ensuring transparency and reproducibility. S/U or letter grading.</p>

Bioinformatics, Graduate

College of Letters and Science

Bioinformatics

BIOINFO M291 - Systems Biology: Practical Data Analysis

Set up reproducible Python-based analysis workflows in Jupyter notebooks

Build and evaluate supervised learning models for classification problems.

Apply linear, logistic, and permutation-based models to omics data.

Communicate scientific results through code, figures, and written explanations.

Use AI coding tools to efficiently prototype and debug biological analyses.

Perform dimensionality reduction and clustering on high-dimensional datasets.

General Catalog

Systems Biology: Practical Data Analysis

BIOINFO M291

4 units