Computational Methods for Biological Networks (Sept. 2019 - Dec. 2019)

1. Instructor

2. Lectures

3. Course Overview and Goals

    In the past decades, vast amounts of biological network data have been generated, which allow increasing numbers of system level studies of biological structures and processes. And many mathematical and computational tools are being developed to analyze these data with the goal of better understanding of biological processes, disease, and contributing to the time and cost effectiveness of biological experimentation. This course will give an overview of the existing types of biological network data, highlight the sources of errors and biases in the data, and introduce in detail the major methods and techniques for analyzing and mining these data.

4. Prerequisites

You should have exposure to at least some of the following:

5. Recommended Texts

6. Grading Scheme

7. Topics Outline

The course will cover the following topics:

8. Lecture Notes

Lec# Content Lecture noted download
1 Intro to the Course
Intro to Biological Networks
Lecture 0
Lecture 1
2 Intro to Molecular Biology Lecture 2
3 Topological properties Lecture 3
4 Network Models, Motifs and Clustering Lecture 4
5 Protein to Protein Interactions Lecture 5
6 Detecting essential proteins Lecture 6
7 Protein Function Prediction
8 Random Walk and Its Applications
9 Graph Embedding and Its Applications

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