From Artificial Intelligence to Network Medicine: New ways to understand and treat diseases

Outline

Artificial Intelligence (AI) is transforming biomedicine by enabling data-driven approaches to disease understanding, clinical decision support, and therapeutic discovery. This seminar provides an overview of AI methodologies applied to biomedical data, from classical machine learning to deep learning, foundation models, and large language models, highlighting both their potential and their limitations in healthcare contexts.

The talk focuses on drug repurposing as a key application, illustrating how AI and network medicine can jointly model diseases, drugs, and molecular mechanisms as interconnected systems. Network-based representations and proximity measures are introduced to identify repurposing opportunities, followed by recent advances using Graph Neural Networks to predict novel disease–drug associations from heterogeneous biomedical knowledge.

Through selected case studies, the seminar demonstrates how integrating AI with network medicine offers a escalable and mechanistic framework to accelerate drug discovery and improve our understanding of complex diseases.

Assessment Method

Attendance and participation. Short assignment.

Lective hours

Remarks

During the course Moodle will be used, so prior to the start of the course it will be necessary for the students to be logged into Moodle with their University credentials.

Timetable

• 06 February, 15:00-18:00

Lecture Theatre

• A-6205

Tuition Language

English.