New tools & research strategies in personalized health

We will define the concept of personalized health, describe the underlying technologies, the technological, legal and ethical challenges that the field faces today, and how they are being met. You will research and prepare a project and defend it orally.

In line with the Health 2030 Initiative (https://health2030.ch/), this course aims to be multidisciplinary, to tackle different aspects of personalized health. Under supervision of a PI to coach you, you will study a specific problem, evaluating its basis and proposing solutions. The output will be the oral defense of your interdisciplinary project.

LEARNING PREREQUISITES
Required courses: none

By the end of the course, the student must be able to:

• Expound the meaning of personalised healthcare
• Interpret data in published papers
• Design a research project
• Present a research project orally
• Contextualise data with respect to the state of the field
• Defend a research project
• Synthesize published data to produce a project
• Give an example of personalised healthcare
  
  TRANSVERSAL SKILLS
  
• Summarize an article or a technical report
• Make an oral presentation
• Write a literature review which assesses the state of the art
• Write a scientific or technical report
• Manage priorities
• Take feedback (critique) and respond in an appropriate manner
• Use a work methodology appropriate to the task
• Assess progress against the plan, and adapt the plan as appropriate
• Collect data
• Continue to work through difficulties or initial failure to find optimal solutions
• Use both general and domain specific IT resources and tools

Ex-cathedra lectures. Group work on specific projects.

• Attend all the lectures.
  
• Preparation of research project in the field of personalized medicine.
  
• Oral presentation/defense of research proposal.
• Active participation to discussions.
• Work in small groups.
  

Written: a mid-term individual assessment (MCQ) based on the lectures (30% of final grade).
Oral: presentation of a project: quality of slides, clarity and content of presentation, ability to answer questions (70% of final grade).
Research summary: relevance to personalized medicine, explanation of relevant background, explanation of research strategy. Multidisciplinary approach.

TAs for group work and occasional meeting(s) with the PI coach.

Bibliography: no prerequisite.
Notes/Handbook: when possible, copies of the slides will be provided.

Marc Friedli & Didier Trono

Marc Friedli, PhD, Manager of Health 2030. Marc Friedli obtained his  PhD in Biology from the University of Geneva, specialising in transgenesis and functional genomics. He completed postdoctoral research at the EPFL, with a focus on epigenetics, stem cells, and transposable elements. As the Program Manager for the Health 2030 Initiative at EPFL's Unit of Precision Medicine, Marc drives genomics research, education, and clinical applications. He helped establish the Genome Center as a national hub and collaborated with organisations like SPHN, PHRT, and the Federal Office of Public Health. Marc was instrumental in Switzerland's genomic response to COVID-19, setting up a testing center at EPFL and coordinating the FOPH-funded national genomic surveillance program since 2021, integrating advanced science  to improve public health.

Katrin Männik was trained in biomedicine, molecular diagnostics and gene technology. She received a PhD from the University of Tartu in 2012. Following post-doctoral training at the Center for Integrative Genomics at the University of Lausanne, she worked as a Research Associate at the University of Lausanne and at the Estonian Genome Center (Estonian population-based biobank). She joined the Health 2030 Genome Center in November 2019.

Her research, supported by the European Commission, the Swiss National Science Foundation and the Jacobs Foundation, has been mainly focused on genetic variants underlying neurodevelopmental disorders and variability of complex traits in the population.

She has authored and co-authored numerous studies published in the top scientific and medical journals (e.g. JAMA, Nature, Am J Hum Genet), and presented platform talks in main international scientific conferences in the field of human genetics and personalized medicine. 

Besides basic research she has a longtime interest in translational and ethical aspects related to the usage of genetic data in healthcare. Katrin Männik has been participating in various initiatives aiming to reach out to the medical community and to society. She is actively involved in national and international projects that work towards maximizing the impact of research and setting the ground for the emergence of personalized medicine (© Campus Biotech).

Raphael Gottardo is a Full Professor of Biomedical Data Science at the University of Lausanne and the founding director of the Biomedical Data Science Center at the University Hospital of Lausanne. Before that, he was a Full Professor in the Vaccine and Infectious Disease Division at Fred Hutch, an affiliate Professor of statistics at the University of Washington, and scientific director of the Fred Hutch Translational Data Science Integrated Research Center. Raphael’s current research interests center on developing computational and statistical tools for analyzing high-dimensional immunological data with applications to vaccine research and immunotherapy. He has led a large number of projects funded by the National Institutes of Health, the Gates Foundation, and the Chan-Zuckerberg Initiative, to name a few. In 2018, he received the Mortimer Spiegelman Award, which honors a statistician below 40 who has made outstanding contributions to health statistics.

Marcel Salathé is a Swiss digital epidemiologist and associate professor at EPFL, where he leads the Lab of Digital Epidemiology and co-directs the EPFL AI Center. His research explores the intersection of epidemiology, artificial intelligence, and digital technology to address public health challenges. During the COVID-19 pandemic, he became a prominent voice in Swiss media, providing scientific insights and policy guidance. In 2020, he was appointed director of the steering committee for the Swiss COVID-19 national research program.

Marcel is a pioneer in digital epidemiology, coining the term in a 2012 publication. His work focuses on analyzing digital data streams, such as social media and mobile phone data, to gain epidemiological insights. He played a key role in developing digital contact tracing systems, notably the SwissCovid app—the first government-backed app based on Google’s and Apple’s Exposure Notifications System.

In recent years, his research has expanded into AI-driven nutritional epidemiology, developing digital tools for high-resolution food tracking in medical cohorts. Through his work, Salathé continues to advance data-driven approaches to public health and disease prevention.

Flavia Hodel is a researcher and data analyst with a strong background in computational and quantitative biology, specializing in human genomics. She holds a Ph.D. in Computational and Quantitative Biology (EDCB) from EPFL, where her doctoral research focused on using genome-wide genotyping data, bioinformatics tools, and statistical methods to investigate the interplay between human genetic variation, persistent infections, and chronic inflammation, particularly in relation to coronary artery disease.

Since 2022, Flavia has been working as a Data Analyst in Human Genomics at CHUV, within the Precision Medicine Unit. Her work centers on population pharmacogenetic studies, exploring the genetic determinants of therapeutic response and drug toxicity under real-life clinical conditions.

In 2024, she co-founded Diabète Type Sport, a non-profit association that brings together healthcare professionals and individuals living with diabetes. The organization is dedicated to promoting physical activity among adults with diabetes through sports events, races, and tailored training sessions, fostering a supportive community aimed at improving overall well-being.

Flavia's expertise spans human genomics, computational biology, and precision medicine, with a strong commitment to advancing research and community health initiatives.

Group Work 1/3

No class scheduled—group work can be completed at your convenience.

Group Work 2/3

No class scheduled—group work can be completed at your convenience.

Group Work 3/3

No class scheduled—group work can be completed at your convenience.

Ascension