Machine Learning in Bioinformatics

Máster en Bioinformática Aplicada a Medicina Personalizada y Salud (Curso 2023-2024)

Scheduling

First week (12-16 February 2024)

• Day 1 (12.02.2024):
  • 2/3 Theory
  • 1/3 Project: Session I (project presentation)
• Day 2 (13.02.2024):
  • 1/2 Theory
  • 1/2 Project: Session II
• Day 3 (14.02.2024):
  • 2/3 Theory
  • 1/3 Project: Session III (with problem and dataset presentation HITO-1)
• Day 4 (15.02.2024):
  • Practice: Hands-On (Machine Learning Basics in Python with scikit-learn Part I)
• Day 5 (16.02.2024):
  • Practice: Hands-On (Machine Learning Basics in Python with scikit-learn Part II)

Second week (26-28 February 2024)

• Day 6 (26.02.2024):
  • 1/2 Theory: presentation of a real case-study (the PolyDeep project)
  • 1/2 Project: Session IV
• Day 7 (27.02.2024):
  • Project: Session III
• Day 8 (28.02.2024):
  • Project Session: IV (with results presentation HITO-2)

Theory

The theory slides are available here.

Practice

Install Docker in your system

Follow the steps in the official page to install Docker https://docs.docker.com/engine/install/ubuntu/

Make sure the docker user group exists.

sudo groupadd docker

Add your user to the docker group.

sudo usermod -aG docker $USER

Reboot the system and the changes will be applied.

Running the Docker image

Run the following commands to pull the Docker image for the hands-on practice sessions:

docker pull singgroup/jupyter-machine-learning:2023-2024

During the hands-on sessions (Machine Learning Basics in Python with scikit-learn), we are going to use the Breast Cancer Data available at the UCI Machine Learning Repository. More information about this dataset can be found here and here.

Go to an empty folder and run the following commands to download the data:

mkdir data

wget https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/wdbc.data -O data/wdbc.data

sed -i '1iid,diagnosis,radius_mean,texture_mean,perimeter_mean,area_mean,smoothness_mean,compactness_mean,concavity_mean,concave points_mean,symmetry_mean,fractal_dimension_mean,radius_se,texture_se,perimeter_se,area_se,smoothness_se,compactness_se,concavity_se,concave_points_se,symmetry_se,fractal_dimension_se,radius_worst,texture_worst,perimeter_worst,area_worst,smoothness_worst,compactness_worst,concavity_worst,concave points_worst,symmetry_worst,fractal_dimension_worst' data/wdbc.data

Alternatively, the file is also available here.

Clone or download this repository and then run the following command (from the local repository) to use the Docker image to start a Jupyter notebook with the required libraries already installed:

docker run --rm -p 8888:8888 -v "$(pwd)":"$(pwd)" -w "$(pwd)" singgroup/jupyter-machine-learning:2023-2024

And then access http://127.0.0.1:8888/lab in a web browser. The resources/notebook-ml-1.ipynb file contains the notebook to develop during these sessions.

Project

The information about the project is available here.

References

• Practical Statistics for Data Scientists: 50 Essential Concepts
• Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition

Additional Resources

• Ten quick tips for machine learning in computational biology [10.1186/s13040-017-0155-3]
• LIBSVM -- A Library for Support Vector Machines
• scikit-learn: machine learning in Python