We examine modern challenges in modeling and understanding complex biological systems through data. High-throughput molecular measurements have necessitated development and application of statistics and machine learning, giving rise to computational biology. Microarray and sequencing technologies enable us to quantify how complex systems are responding to and influenced by experimental and external conditions. It may lead to better understanding fundamental organizational principles and functionalities of molecules and cells. Lately, there have been interesting developments in single cell analyses, spatial genomics, imaging and others that involve higher resolutions, scales, and complexities.
In this course, we study exploratory data analysis, statistical learning, and neural networks that are specifically designed for such biological studies. Good understanding of statistics and programming are prerequisites. Students will program in R and Python, read primary literature weekly, and complete data analysis projects.