Machine Learning for Biosciences

or

Curriculum Module

Created with R2025a. Compatible with R2025a and later releases.

Information

This curriculum module contains interactive MATLAB® live scripts that introduces basic machine learning algorithms and apply them to biological datasets .

Background

You can use these live scripts as demonstrations in lectures, class activities, or interactive assignments outside class. This module covers machine learning applied to various biosciences datasets.

The instructions inside the live scripts will guide you through the exercises and activities. Get started with each live script by running it one section at a time. To stop running the script or a section midway (for example, when an animation is in progress), use the Stop button in the RUN section of the Live Editor tab in the MATLAB Toolstrip.

Contact Us

Solutions are available upon instructor request. Contact the MathWorks teaching resources team if you would like to request solutions, provide feedback, or if you have a question.

Prerequisites

This module assumes basic MATLAB® knowledge and we recommend that all students take the MATLAB Onramp before continuing if they have not already.

Getting Started

Accessing the Module

On MATLAB Online:

Use the link to download the module. You will be prompted to log in or create a MathWorks account. The project will be loaded, and you will see an app with several navigation options to get you started.

On Desktop:

Download or clone this repository. Open MATLAB, navigate to the folder containing these scripts and double-click on MachineLearningBiosciences.prj . It will add the appropriate files to your MATLAB path and open an app that asks you where you would like to start.

Ensure you have all the required products (listed below) installed. If you need to include a product, add it using the Add-On Explorer. To install an add-on, go to the Home tab and select Add-Ons > Get Add-Ons.

Products

MATLAB® is used throughout. Tools from the the Statistics and Machine Learning Toolbox™ , Deep Learning Toolbox™ are used frequently as well.

• MATLAB®
• Deep Learning Toolbox™
• Statistics and Machine Learning Toolbox™

Scripts

An Overview of Machine Learning for Science and Engineering

	In this script, students will... $\bullet$ explain the primary goal of machine learning. $\bullet$ distinguish between supervised and unsupervised learning. $\bullet$ describe the key steps in a typical machine learning workflow.	Academic disciplines $\bullet$ Biosciences $\bullet$ Biology $\bullet$ AI

Unsupervised Learning

	In this script, students will... $\bullet$ apply Principal Component Analysis (PCA) to reduce dimensions of biosciences data. $\bullet$ use k-means clustering to identify natural groupings in unlabeled data. $\bullet$ evaluate clustering performance using confusion matrices.	Academic disciplines $\bullet$ Biosciences $\bullet$ Biology $\bullet$ AI

Supervised Learning: Classification

	In this script, students will... $\bullet$ train and evaluate models to classify disease using supervised learning. $\bullet$ assess model performance with accuracy, confusion matrices, and ROC curves. $\bullet$ improve accuracy using feature selection, PCA, and cost weighting.	Academic disciplines $\bullet$ Biosciences $\bullet$ Biology $\bullet$ AI

Supervised Learning: Regression

	In this script, students will... $\bullet$ use supervised learning to train and evaluate regression models that predict mollusk age. $\bullet$ evaluate and compare models using statistical performance metrics such as accuracy, confusion matrices, and RMSE. $\bullet$ apply machine learning in biosciences.	Academic disciplines $\bullet$ Biosciences $\bullet$ AI

Unsupervised Learning Problem Set

	In this script, students will... $\bullet$ apply unsupervised learning to a cancer cell dataset.	Academic disciplines $\bullet$ Biosciences $\bullet$ Biology $\bullet$ AI

License

The license for this module is available in the LICENSE.md.

Related Courseware Modules

Machine Learning Methods: Clustering

	Available on: GitHub

Or feel free to explore our other modular courseware content.

Educator Resources

• Educator Page

Contribute

Looking for more? Find an issue? Have a suggestion? Please contact the MathWorks teaching resources team. If you want to contribute directly to this project, you can find information about how to do so in the CONTRIBUTING.md page on GitHub.

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