Computational Gene Expression Analysis with Python
Computational Gene Expression Analysis with Python, available at Free, has an average rating of 4.5, with 19 lectures, based on 258 reviews, and has 6307 subscribers.
You will learn about Bioinformatics Gene Expression Computational Biology Network Analysis GEO2R STRING Network Analysis KEGG Pathways Microarray Proteins DNA RNA Transcriptomics Research Biotechnology Python Programming This course is ideal for individuals who are Middle and high school students interested in completing an original computational biology science project or Students interested in bioinformatics and computational biology It is particularly useful for Middle and high school students interested in completing an original computational biology science project or Students interested in bioinformatics and computational biology.
Enroll now: Computational Gene Expression Analysis with Python
Summary
Title: Computational Gene Expression Analysis with Python
Price: Free
Average Rating: 4.5
Number of Lectures: 19
Number of Published Lectures: 19
Number of Curriculum Items: 19
Number of Published Curriculum Objects: 19
Original Price: Free
Quality Status: approved
Status: Live
What You Will Learn
- Bioinformatics
- Gene Expression
- Computational Biology
- Network Analysis
- GEO2R
- STRING Network Analysis
- KEGG Pathways
- Microarray
- Proteins
- DNA
- RNA
- Transcriptomics
- Research
- Biotechnology
- Python
- Programming
Who Should Attend
- Middle and high school students interested in completing an original computational biology science project
- Students interested in bioinformatics and computational biology
Target Audiences
- Middle and high school students interested in completing an original computational biology science project
- Students interested in bioinformatics and computational biology
TLDR: Learn to analyze and quantify differences in gene expression using public datasets from the Gene Expression Omnibus. Obtain a detailed understanding of how gene expression analysis works, i.e. what is fold change? See examples of how Python can be used to analyze and visualize gene expression data.
You will learn how to use tools like GEO2R, StringDB, PantherDB, and more to analyze publicly available gene expression data!
The course will guide you on choosing a researchtopic, finding a dataset, processing the data, and analyzingthe data graphically with several tools, like StringDB. As a bonus, you will get insight into how to write a paper about your project.
Example topics for research include:
-
Identifying potential biomarkersfor cancer (useful in diagnostics)
-
Analyzing changes in gene expression when a sample is treated with X drug or under Y condition
-
Differences in gene expression between early and late stage cancer (useful in prognosis and drug development)
The example project being done in this course is for identifying blood biomarkers for early stage Parkinson’s disease.
Materials needed:
1. Computer
2. Google Account (for Google Sheets + Colab) or Excel
3. Internet Connection
If there is enough interest, another course will be created that features gene expression analysis with machine learning.
Course Curriculum
Chapter 1: Introduction
Lecture 1: Introduction
Lecture 2: Syllabus/What to Expect
Lecture 3: Overview of Basic Biology Concepts
Chapter 2: Finding a Dataset and Using GEO
Lecture 1: IMPORTANT NOTE
Lecture 2: Intro to Gene Expression Omnibus
Lecture 3: What is the Gene Expression Omnibus?
Lecture 4: Finding a Dataset
Lecture 5: Using GEO2R
Lecture 6: Review of GEO2R
Lecture 7: Data Manipulation
Chapter 3: Using Bioinformatics Tools
Lecture 1: String DB
Lecture 2: PantherDB
Lecture 3: Researching Individual Genes
Chapter 4: Python for Data Analysis and Visualization
Lecture 1: Google Colab, a Web-based Python IDE
Lecture 2: Lung Cancer miRNA analysis with Python
Lecture 3: Volcano Plot with Python
Lecture 4: Most Significant Biomarkers Barplot with Python
Chapter 5: Next Steps
Lecture 1: What to Do From Here?
Lecture 2: Thank you!
Instructors
-
Andrew Gao
Researcher -
Sarah Gao
Instructor at Udemy
Rating Distribution
- 1 stars: 4 votes
- 2 stars: 14 votes
- 3 stars: 41 votes
- 4 stars: 84 votes
- 5 stars: 115 votes
Frequently Asked Questions
How long do I have access to the course materials?
You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!
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