Introduction to bioinformatics and AI pipelines

Short description of the course:

The “Introduction to Bioinformatics and AI Pipelines” course will provide students with foundational knowledge and practical skills in bioinformatics, focusing on the integration of artificial intelligence (AI) in analyzing biological data. The course will cover essential concepts in genomics, transcriptomics, and proteomics, along with data preprocessing, feature selection, and machine learning techniques for biological data analysis. Students will learn to develop AI pipelines for tasks such as predicting disease-related biomarkers and modeling complex biological systems. Emphasis will be placed on hands-on experience using bioinformatics tools, programming, and AI frameworks to solve real-world problems in life sciences.

Learning outcomes:

By the end of the “Introduction to Bioinformatics and AI Pipelines” course, students will gain the ability to understand fundamental principles of bioinformatics, including genomics, transcriptomics, and proteomics, and recognize their significance in biological and medical research. They will be able to develop and implement AI-based pipelines for analyzing biological data, such as sequence analysis, biomarker discovery, and disease prediction. Students will acquire skills in data preprocessing, cleaning, transformation, and feature selection to prepare biological datasets for machine learning models. They will also learn to use bioinformatics tools, programming languages (e.g., Python, R), and libraries (e.g., scikit-learn, TensorFlow) to conduct data analysis and develop AI pipelines. The course will enable students to analyze large-scale omics datasets and interpret results to understand biological mechanisms or identify potential therapeutic targets. They will be equipped to integrate multiomics data for modeling complex biological systems, uncovering interactions that influence health and disease.