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Copyright (c) 2019 OERCompBiomed - Open Educational Resources in Computational Biomedicine

Introduction to Computational Biomedicine
and Machine Learning

CBM101 image

This is the repository for the course CBM101: Introduction to Computational Biomedicine and Machine Learning, a collaboration within the NordBiomed network.

Here you find exercises, code and documentation for the course. You will find more information about the Summer school testing this course at (e-learning modules will later be avaialble on the Open edX platform at Bibsys).

Quick Start

If you want to start right away, watch this video (and optionally this one first if you are new to computer science).



Instructions for users

Instructions for teachers


The big picture of CBM101

CBM101 is part of the "Open Educational Resources in Computational Biomedicine"* (OERCompBiomed) project conducted by the and funded by Erasmus+.

NordBioMed is a collaborative network in the field of Biomedicine(*) between the Universities of Turku, Eastern Finland (Kuopio), Bergen, Odense and Karolinska Institutet. The network was originally formed in 2013 to strengthen the individual biomedical teaching programs within the component universities and make them internationally more competitive by providing complementary activities from the partner universities. The network supports both student and teacher mobility, organises intensive courses and develops virtual online teaching and an information platform on the Open edX platform, supported by a GitHub repository. Links that redirects to the study programme pages of each NordBioMed partner universities can be found here.

(*) Biomedicine covers those areas of human biology, chemistry and medicine that seek to explain the factors behind health and disease at the molecular and cellular level. This information is applied in the development of better diagnostics and treatments.

What’s OERCompBiomed?

The Nordic network of Biomedicine educators NordBioMedNet has received a grant of 350 000 euros from Erasmus+ to develop biomedicine education. With the received money the network can start providing Open Educational Resource (OER) courses that are open for everybody. They will start by providing courses of Biomedical Ethics, Digital Pathology, Computational Biomedicine and machine learning.

The main objective of the project is to provide students in the field of biomedicine with modern, timely, up-to-date, and professionally relevant learning experiences that enable them to develop skills and competences in biomedical data management and use, and skills and competences to identify, analyse and handle ethical challenges within modern biomedicine.

As modern biomedical research produces massive data generated by high-throughput methods, students need to develop computational and analytical skills to manage and utilise “big data”. Moreover, knowledge and tools in bioethics are also increasingly important due to present rapid technological development in biomedicine with, for example, a new era of modern genomic/genetic research ripe with very critical and difficult ethical issues.

Excerpt from "Erasmus+ funding for development of Biomedical education" An interview with Merja Heinäniemia

You can read more about OER in this Foundations for OER Strategy Development document.

Submodules in the "Introduction to Computational Biomedicine and Machine Learning" part of OERCompBiomed project:

  • Introduction: Motivation to study computational methods in the life sciences.

  • Python and Friends: Introduction to programming in Python and specialized libraries (Numpy and Pandas).

  • Data Resources: Covers some basics of accessing datasets from online via Python.

  • Network Analysis: Fundamental concepts in network science and application to real and toy biological problems.

  • Biostatistics: Statistical analysis of (bio)medical data in R.

  • Machine Learning: Unsupervised and supervised learning with applications to biological data analysis.

  • Neural Networks: Core concepts underlying deep learning with some basic application to imaging data.

  • Image Analysis Covers the fundamentals of medical image processing and analysis, with a focus on MRI.

  • Variational Inference Optimization with Bayesian Methods Bayesian methods applied to autoencoders in Pytorch (advanced).

Inside each corresponding directory, you will find a set of interative notebooks with code material and exercises, each covering a specific subtopic. The order of the submodules is structured generally in an increasing level of difficulty, but there is no requirement to follow them in the given order.

Instructions for users


You need to set up the Jupyter enrivonment on your computer or use one of the cloud options (e.g. Binder) to run the exercise material.

Follow the instructions at Setting up your system to get ready

You will have to install different packages (mostly Python) in your system along the course.

Note: To access the course notebooks interactively without downloading any software we are planning to use Binder.

Note: For the more advanced modules of this program, you will need to have the jupyter notebook environment working on your commputer

Jupyter notebooks

The course is based on Jupyter Notebooks, a web-based framework for developing and presenting code-based projects (take a look at og for introductions to Jupyter Notebooks). You can read more about why scientists chose Jupyter notebooks here:

Throughout the course you will work with notebooks that contain various material and programming tasks. We recommend that you make a copy of our notebooks before you are editing them. In this respect you might adopt the naming convention my_[name_of_notebook].ipynb.

Instructions for teachers

This course is created and maintained as an international effort. The following guidelines describe the organization of the teaching material.

General layout

The topics and subtopics mirrors the structure on Bibsys. Each exercise notebook should have a specific entry with a link to it in Bibsys.

Solutions to exercises

Exercises inside of notebooks are provided with solutions, with an option to load these solutions using the %load magic command. At this early stage, the solutions are already loaded for most notebooks, so make sure you remove these (leaving the %load line) prior to distributing material to students. You can fork the repo and make your edits there.

How to add a new exercise

When adding a new notebook, try to ensure both naming convention and content is in a style coherent to the remaining repository. A typical notebook should follow the structure of the given template.

  • Create folder within the correct topic if necessary
  • Separate notebooks into small tasktopics
  • Follow the structure of the notebook template and include exercises Note: For comments, bug reports and suggestions use the Issues


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