Applied Machine Learning for Bioinformatics

Course Overview

This online workshop provides a practical introduction to machine learning (ML) techniques for bioinformatics applications. Designed for students, researchers, and professionals in life sciences and computational biology, the course covers essential ML concepts and their use in analyzing biological data. Participants will begin with an overview of ML in bioinformatics, followed by key steps in data preparation and feature engineering to ensure high-quality inputs for models. The course explores supervised learning methods such as classification and regression for predicting biological outcomes, as well as unsupervised learning techniques like clustering for pattern discovery in genomic and proteomic data. Participants will also learn about model evaluation and interpretation , ensuring reliable and explainable results. The hands-on session includes setting up an ML environment, exploring and preprocessing biological datasets, engineering relevant features, and building both supervised and unsupervised learning models. By the end of the workshop, participants will have a solid foundation in applying machine learning to bioinformatics and be equipped with practical skills to analyze complex biological data. This online workshop provides a practical introduction to machine learning (ML) techniques for bioinformatics applications. Designed for students, researchers, and professionals in life sciences and computational biology, the course covers essential ML concepts and their use in analyzing biological data. Participants will begin with an overview of ML in bioinformatics, followed by key steps in data preparation and feature engineering to ensure high-quality inputs for models. The course explores supervised learning methods such as classification and regression for predicting biological outcomes, as well as unsupervised learning techniques like clustering for pattern discovery in genomic and proteomic data. Participants will also learn about model evaluation and interpretation , ensuring reliable and explainable results. The hands-on session includes setting up an ML environment, exploring and preprocessing biological datasets, engineering relevant features, and building both supervised and unsupervised learning models. By the end of the workshop, participants will have a solid foundation in applying machine learning to bioinformatics and be equipped with practical skills to analyze complex biological data. Introduction to Machine Learning in Bioinformatics Data Preparation & Feature Engineering Supervised Learning for Bioinformatics Unsupervised Learning for Bioinformatics Model Evaluation & Interpretation Hands-on: Setting Up ML Environment & Data Exploration, Data Preprocessing & Feature Engineering, Building a Supervised Learning Model, Unsupervised Learning for Clustering

Sub-topics

• 1.
  Introduction to Machine Learning in Bioinformatics

• 2.
  Data Preparation & Feature Engineering

• 3.
  Supervised Learning for Bioinformatics

• 4.
  Unsupervised Learning for Bioinformatics

• 5.
  Model Evaluation & Interpretation

• 6.
  Hands-on: Setting Up ML Environment & Data Exploration, Data Preprocessing & Feature Engineering, Building a Supervised Learning Model, Unsupervised Learning for Clustering

Course Components