Raul Rabadan is the Gerald and Janet Carrus Professor in the Departments of Systems Biology, Biomedical Informatics and Surgery at Columbia University. He is the director of the Program for Mathematical Genomics at Columbia University and he was the Director of the NCI Center for Topology of Cancer Evolution and Heterogeneity at Columbia University (2015-2021). From 2001 to 2003, Dr. Rabadan was a fellow at the Theoretical Physics Division at CERN, the European Organization for Nuclear Research, in Geneva, Switzerland. In 2003 he joined the Physics Group of the School of Natural Sciences at the Institute for Advanced Study. Previously, Dr. Rabadan was the Martin A. and Helen Chooljian Member at The Simons Center for Systems Biology at the Institute for Advanced Study in Princeton, New Jersey. He has been named one of Popular Science’s Brilliant 10 (2010), a Stewart Trust Fellow (2013), and he received the Harold and Golden Lamport Award at Columbia University (2014) and the Diz Pintado award (2018). Dr. Rabadan’s current interest focuses on uncovering patterns of evolution in biological systems through the lens of genomics. His recent interests include the development of mathematical approaches to uncover the evolution of cancer and infectious diseases, including topological data analysis and Random Matrix Theory, among others.
Cesar Hidalgo directs the Center for Collective Learning at ANITI, University of Toulouse. He directed the Collective Learning group at MIT, holds a PhD in Physics from the University of Notre Dame, and is the author of dozens of papers & three books.
ICREA-IMIM, Barcelona, ICREA Research Professor
Research in recent years has uncovered the existence of a large non-canonical proteome that mostly consists of proteins smaller than 100 amino acids. In humans alone, thousands of non-canonical proteins have been discovered using ribosome profiling and proteomics techniques. These proteins are translated from alternative open reading frames or from regions previously believed to be non-coding, such as long non-coding RNAs (lncRNAs) and untranslated regions (UTRs). A subset of the small proteins show strong phylogenetic conservation and are involved in fundamental cellular functions. Another large fraction is species- or lineage-specific, and represents a much more rapidly evolving part of the proteome than remains poorly characterized. Using studies in yeast, we provide evidence that these proteins are important for short evolutionary time scale adaptations.
We also show that they can provide plenty of raw material for de novo gene birth, a process by which proteins with completely new sequences emerge from previously non-coding parts of the genome.
IBM Research Europe
Technical Leader of Systems Biology at IBM Research Europe (Switzerland), associated member of the Department of Biology at ETH and editor for ImmunoInformatics and Frontiers in Systems Biology.
CSE Department, INESC-ID/IST Technical University of Lisbon (Portugal)
Senior research member (PI) at the Information and Decision Support Systems Group/INESC-ID
Like in many other industries, the healthcare sector routinely generates vast amounts of data from many different sources ranging from biochemical exams, electronic medical records, vital signs, patient-reported outcomes, health surveys, clinical trials, insurance claims, administrative data, and more recently omics. These days, large volumes of data associated with the new technologies of artificial intelligence are promising to create the foundations for a new paradigm of medicine focused on the individuality of each person.
In this talk I will discuss different approaches that are being tested in EU hospitals with the goal to transform healthcare from reactive disease care to care that is patient or person-centered and focused on disease prevention. A special focus will be given to the use of polygenic risk scoring models in the implementation of genetic panels that can support health professionals in disease prevention. Additionally, I will discuss how the field of pharmacogenomics is being able to make its way from research to clinical practice, which could become, in the short term, the first pillar of the democratization of preventive and personalized medicine.
Since artificial intelligence is becoming a disruptive technology in the healthcare sector, it is also crucial to address the ethical and legal challenges imposed by this new technological advance.