Basic Python/Machine Learning in Bioinformatics
Please download the kaggle for the code
Created by William Kang - Bioinformatics Researcher
Students: 6961, Price: Free
This is a course intended for beginners interested in applying Python in Bioinformatics. We will go over basic Python concepts, useful Python libraries for bioinformatics/ML, and going through several mini-projects that will use these Python/ML concepts. These mini-projects include a sequence analysis (with no libraries) Python example, a Python sequence analysis example using libraries, and a basic Sklearn Machine Learning example.
Artificial Intelligence In Bioinformatics
Take the first step into machine learning for bioinformatics with a step-by-step approach
Created by Yash Gupta - Chief Technical Officer at The Helyx Initiative
Students: 3899, Price: Free
This free course is heavily based on the ideology of "learning by doing" and, as a result, will be very hands-on. Students will learn a lot more about the emerging field of the future, artificial intelligence, and machine learning, through a series of mini-projects relating to biology in a field known as bioinformatics that can be expanded on after the course. As a result of the projects being done throughout the course, students will also gain further research and publication ideas, such as science fairs and journals. These projects include:
Using breast cancer dimension data to classify breast cancer
Classifying bioassay data
Detecting pneumonia by analyzing lung images with convolutional neural networks
Detecting malaria by analyzing skin images
Gene Expression Analysis Science Project (Step by Step)
Learn how to do a novel bioinformatics research project all on your computer! Great for high school students.
Created by Andrew Gao - Bioinformatics Researcher
Students: 2548, Price: Free
This FREE course is perfect for teens curious about biotechnology and eager to conduct a virtual science project with original findings.
It takes LESS THAN TWO HOURS!
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 research topic, finding a dataset, processing the data, and analyzing the data graphically. As a bonus, you will get insight into how to write a paper about your project.
Example topics for research include:
Identifying potential biomarkers for 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.
2. Google Account or Excel
3. Internet Connection
If there is enough interest, another course will be created that features gene expression analysis with machine learning, Python, R, and other computer science techniques.