Overview
Study the use of AI, machine learning, and deep learning in bioinformatics
This course will teach you the fundamentals of how AI is applied in the field of bioinformatics.
You’ll explore how AI, machine learning, deep learning, and natural language processing (NPL) concepts are used in the design and discovery of drugs, as well as for modelling complex biological systems.
Learn about AI-based bioinformatics
Artificial intelligence (AI) is transforming the field of bioinformatics.
On this course, you’ll learn the basics of collecting, analysing, and modeling bioinformatics data using AI.
You’ll find out how to collect and explore bioinformatics data from public resources and then use AI to analyse and model this data in order to better understand key biological processes.
Discover AI applications in bioinformatics research, such as genome sequencing
As you develop your knowledge of both the bioinformatics and artificial intelligence industries, you’ll explore how different AI concepts are applied in bioinformatics to mathematically interpret biological data and provide statistical information. You’ll develop your understanding of different cases of AI-based bioinformatics research, including genome sequencing, protein function prediction, and gene expression examination.
Build your AI toolkit for working in bioinformatics
This course will provide you with the AI knowledge and skills you need to apply AI in practice.
You’ll build data analytics and visualisation techniques that you can apply to a range of bioinformatics datasets and explore the latest machine learning concepts like feature engineering and feature learning.
You’ll put your learning into practice at the end of the course when you’ll be asked to organise and summarise data results for an academic paper in bioinformatics that’s fit for publication.
This course is designed for any student, biologist, or researcher who would like to learn the basics of using AI to study bioinformatics data.
You do not need any experience in AI, bioinformatics, or programming.
Waikato Environment for Knowledge Analysis (Weka).
Syllabus
- Basic concepts of AI in bioinformatics data
- Introduction of bioinformatics
- Bioinformatics data
- Artificial Intelligence: fundamentals and applications
- Fundamentals of AI and machine learning
- Weka and machine learning algorithms
- Bioinformatics feature engineering
- AI-based bioinformatics workflow
- Bioinformatics feature engineering
- Assessment Time