Advances in gathering, storing, processing and retrieving data are transforming research in biomedicine and public health. These areas have conventionally relied on tools based on sampling with inductive rules to improve the generalisation of conclusions. With current information-handling abilities, we can anticipate the recording of all relevant data from entire populations, making unnecessary the need for sampling and its framework of rules.
The feasible move by governments and health agencies towards 'cradle to grave' electronic health records for entire populations opens up the prospect of intelligent systems able to identify health risks and useful interventions for population subsets and eventually for individual people. The development of intelligent technology to detect, evaluate and represent patterns in data will underpin these advances in health management.
In the near term, applications are likely in the development of new medicines, perhaps with less reliance on a priori mechanistic knowledge, and greater use of pattern recognition to indicate where new discoveries might be made, stimulating new mechanistic insights. In medicines development, stratification of patient populations in clinical trials, before a product goes to market, has large potential to produce safe, effective and cost-effective therapeutics for diseases where current treatments are unsatisfactory.
Pattern recognition of predictors of disease susceptibility, responses to medicines, and more widely in the fields of nutrition and lifestyle, would transform our ability to prevent, cure or mitigate diseases, and our approach to the maintenance of health.
Bio: Gordon Duff graduated in Medicine from Oxford, and St Thomas's Hospital, London, where he also gained a PhD in Neuropharmacology. He held junior faculty posts at Yale University and the Hughes Institute of Molecular Immunology at Yale before joining the Edinburgh Medical School in1984. In 1990 took up his present post of Florey Professor of Molecular Medicine at Sheffield, where he was Research Dean of the Faculty and Director of the Division of Genomic Medicine. He currently chairs the Academic Health Sciences Centre of Trinity College Dublin, and the International SAB of the MRC Centre for Drug Safety Sciences at Liverpool.
Previously Chairman of the UK's Committee on Safety of Medicines and its Biological Sub-committee, he has been Chairman of the UK's Commission on Human Medicines since 2005. In 2006 he chaired the Secretary-of-State's Expert Scientific Group on Phase One Clinical Trials, following the disaster at Northwick Park Hospital. From 2002 to 2009 he was also Chairman of the National Biological Standards Board, overseeing the National Institute for Biological Standards and Control. He is an advisor on Biological Medicines to the EU, and Chairman of the UK's Scientific Pandemic Influenza Advisory Committee. In 2009-10, he co-chaired, with Govt Chief Scientist, the Cabinet Office's Scientific Advisory Group for Emergencies during the pandemic flu outbreak. In 2010 he reviewed the UK's Organ Donor Register at the request of the Secretary-of-State.
He is an Honorary Fellow of St Peter's College, Oxford, Fellow of the Academy of Medical Sciences, Fellow of the Royal Colleges of Physicians of Edinburgh and London (Croonian Lecturer), and Fellow of the Royal Society of Edinburgh. In 2007 he received a Knighthood for services to public health.
Over the past decades a clear paradigm has emerged as large redshift surveys opened the window onto the distribution of matter in our Local Universe: galaxies, intergalactic gas and dark matter exist in a wispy weblike spatial arrangement consisting of dense compact clusters, elongated filaments, and sheetlike walls, amidst large near-empty void regions. The Cosmic Web is the fundamental spatial organization of matter on scales of a few up to a hundred Megaparsec, scales at which the Universe still resides in a state of moderate dynamical evolution.
While the complex intricate structure of the cosmic web contains a wealth of cosmological information, its quantification has remained a major challenge. In this lecture, we describe our effort to measure key topological parameters. To this end, we resort to the homology of the weblike structure, and determine the scale-dependent Betti numbers. For 3-D structures they count the number of components, tunnels and enclosed voids. Out study includes a study of persistence and persistent homology, which entails the conceptual framework for separating scales of a spatial structure. To infer this from the discrete spatial galaxy distribution (or of particles in computer models of cosmic structure formation) we extract the homology from alpha shapes. Alphashapes were introduced by Edelsbrunner to formalize the concept of "shape" for a spatial point dataset. At large value of alpha corresponds to the convex hull of the dataset, while as alpha shrinks the alphashape assumes cavities which may join to form tunnels and voids.
We have studied the alpha complex of the cosmic weblike point patterns, in order to assess the signature of filaments, walls and voids. The physical interpretation of the obtained scale-dependence of Betti numbers is determined from a range of cellular point distributions. The findings from the Voronoi clustering models is used to analyze the outcome of cosmological N-body simulations and the SDSS galaxy redshift survey.
Bio: Rien van de Weygaert studied astronomy and physics at the University of Leiden, where he obtained his PhD cum laude in 1991 on "Voids and the Geometry of Large Scale Structure". Subsequently, he worked as an NSERC fellow at the Canadian Institute of Astrophysics (CITA) in Toronto, Canada and as a research fellow at the Max Planck Institut für Astrophysik in Garching, Germany before taking up a KNAW fellowship at the Kapteyn Astronomical Institute at the University of Groningen. Since 2004 he is professor of cosmological structure formation at the Kapteyn Insitute. His research interests concern cosmology, the large scale Universe and the formation of structure in the Universe, as well as computational geometry and topology and pattern recognition. Within these research subjects he has had a particular interest in the formation and dynamics of the Cosmic Web, the complex network of interconnected filamentary galaxy associations that pervades our universe on scales of tens to hundreds of million lightyears, and the existence and evolution of voids, the large near empty regions in between these structures. For the analysis of this structure, he has been working on a diverse array of tools based on Voronoi and Delaunay tessellations and related geometric concepts, finding that they provide a highly versatile means of tracing the complex structures seen in the Universe. Recently he started to wander into the history of astronomy, via a research project on world's oldest astronomical and mechanical computer, the Antikythera mechanism from ancient Greek times.
We consider the problem of finding information in high-dimensional noisy data. Our goal is to understand the possibilities and limitations of such correlation detection problems. The mathematical analysis reveals some interesting phase transitions. We also discuss an interesting connection with random geometric graphs.
Bio: Gábor Lugosi graduated in electrical engineering at the Technical University of Budapest in 1987, and received his Ph.D. from the Hungarian Academy of Sciences in 1991. Since 1996, he has been at the Department of Economics, Pompeu Fabra University. In 2006 he became an ICREA research professor. His research interest includes learning theory, nonparametric statistics, inequalities in probability, random structures, and information theory.