Workshops and courses

Omics and data-driven precision health

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Date: 6 - 31 October 2025

"Omics and Data-Driven Precision Health" is an interdisciplinary course that bridges the gap between artificial intelligence (AI) and precision health, focusing on the application of AI and machine learning technologies that are transforming personalized healthcare.

Course Level: beginner-intermediate

Register here!

The course content includes introductions to omics technologies and an overview of core AI and ML principles, such as supervised and unsupervised learning, neural networks, and deep learning. Additionally, the applications of AI and ML in multi-omics analysis and for disease diagnostics and treatment optimization will be discussed.

Topics covered include:

● Introduction to Omics Technologies: Overview of the omics field and the development of omics technologies.
● Principles of AI, ML, and Network Analysis: Fundamental techniques and applications in AI and ML.
● Translational Omics and Precision Health: Focus on biomarkers and their role in precision health.
● Applications of AI in Healthcare: Examples of how AI can support diagnosis and treatment.
● Ethical, Legal, and Social Aspects: Discussion on ethical issues related to AI use in healthcare, including privacy and equality.
● Practical Project and Data Analysis: Hands-on experience through a group-based project involving omics data analysis.

Important dates

Application open: 5-April-2025
Application closes: 1-June-2025
Confirmation to applicants: 20-June-2025

Course dates:

  • October 6-10 (On site), October 31 (online)
  • October 6: 09:00-16:30 (Campus US, Linköping)
  • October 7: 09:00-16:30 (Campus US, Linköping)
  • October 8: 09:00-16:30 (Campus US, Linköping)
  • October 9: 09:00-16:30 (Campus US, Linköping)
  • October 10: 09:00-16:30 (Campus US, Linköping)
  • October 31: 09:00-16:30 (Online)

Notes:

  1. The course is highly interactive and hence it is important that you, as a participant, actively contribute to all sessions and elements of the course.
  2. There are no fees for this course.

Entry Requirements

This course is intended for PhD students and researchers who are interested in utilizing omics technologies and AI methods to advance personalized healthcare. Through lectures, workshops, and seminars, participants will explore:
• Which data types are prevalent in population-level omics studies, and how are they generated?
• How to integrate genomics, proteomics, metabolomics, and other omics data to better understand human health and diseases?
• Which AI/ML methods are suitable for multi-omics analysis, and how can they be applied in practice?
• How to identify biomarkers or key features from high-dimensional omics datasets?
• What are the ethical and legal considerations around using patient data and AI in healthcare?

Due to limited space the course can accommodate a maximum of 25 participants. If we receive more applications, priority will be given to candidates who have ongoing or planned projects that utilize omics data and data-driven approaches.

Contact: Wen Zhong (SciLifeLab, DDLS fellow, LiU) wen.zhong@scilifelab.se

Keywords: omics, data science, Machine Learning, precision medicine, translational medicine

Venue: Online, Linköping University Hospital Campus (Campus US)

City: Linköping

Country: Sweden

Prerequisites:

Course prerequisites:

• Experience with either R or Python programming will be helpful for the workshop sessions.
• Each participant needs to bring a personal laptop to participate in the workshops

Learning objectives:

Upon completing the course, students are expected to achieve the following:

Knowledge and Understanding

• Describe the principles of AI applied to precision health and identify the challenges in healthcare that these technologies aim to address.
• Explain the roles of AI technologies in analyzing biomedical data for disease diagnostics, treatment optimization, and the development of personalized healthcare solutions.
• Understand and distinguish the capabilities and limitations of various AI and machine learning (ML) methods in the context of precision health, including supervised and unsupervised learning, neural networks, and deep learning.
• Explain the significance of multi-omics analysis in precision health and how AI technologies facilitate the interpretation of complex biological information.

Skills and Abilities

• Utilize advanced methods for integrating multi-omics data.
• Apply AI and ML methods to solve problems in precision medicine.
• Analyze and interpret biomedical data using AI-driven methods.

Critical Thinking and Approach

• Demonstrate a critical approach when selecting and applying AI and ML methods in biomedical research to ensure the reliability and validity of results.
• Discuss the ethical, legal, and social implications of using AI in healthcare, with a focus on privacy, data security, and equitable access to medical innovations.

Target audience: PhD Students, postdocs, researchers

Event types:

  • Workshops and courses

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