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Online Information 2009 Conference

Online Information 2009 Conference



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Diseasome Diseasome Presentation Transcript

  • Diseasome Future designs of scientific information systems  Online Information 2009, London Mathieu Bastian, Sebastien Heymann INIST‐CNRS, France December 2009
  • Exploring the human disease network The diseasome website is a disease/disorder relationships explorer and a sample of an  innovative map‐oriented scientific work. Built by a team of researchers and engineers, it  uses the Human Disease Network dataset and allows intuitive knowledge discovery by  mapping its complexity. Diseasome – Mathieu Bastian, Sébastien Heymann Online Information 2009, London
  • Original data Official paper The Human Disease Network  Goh K‐I, Cusick ME, Valle D, Childs B, Vidal M, Barabási A‐L (2007) Proc Natl Acad Sci USA 104:8685‐8690 Link: http://www.pnas.org/content/104/21/8685.full Data retrieved as linked data Link: http://www4.wiwiss.fu‐berlin.de/diseasome/ Network‐like organization • 526 diseases and 903 genes in the main sub‐graph • nodes =  disease or gene • edges = gene‐disorder association, reveal a common genetic origin • 22 different categories of diseases: Bone,  Cancer, Cardiovascular etc. Medical application example Understanding the the spread of obesity: NYT visualization, 2007 Network Medicine — From Obesity to the "Diseasome",  Albert‐László Barabási Link: http://content.nejm.org/cgi/content/full/357/4/404 Diseasome – Mathieu Bastian, Sébastien Heymann Online Information 2009, London
  • http://diseasome.eu The website is a portal for online resources based on data exploration, and contains: An interactive map Intuitive access to specific gene/disease documents. The technology is provided by Linkfluence, a  research institute adept in social web studies. A poster Printable network of diseases for collaborative analysis and communication. An expert tool Embedded graph visualization and manipulation software, Gephi, for advanced exploration. A book How information technologies change the way biologists work? Which benefits could we expect? The  Diseasome website offers a practical advocacy for "Biologie ‐ L'ère numérique" (Biology ‐ The digital  era), directed by Magali Roux at INIST‐CNRS. ), y g Diseasome – Mathieu Bastian, Sébastien Heymann Online Information 2009, London
  • Map overview The map contains the diseases and genes  relations, presented with nodes and edges. The nodes represent diseases. White nodes  represent genes. The edges represent correlations  between diseases and genes, or relations between  between diseases and genes, or relations between diseases if they have a gene in common. Node color indicates the category it belongs to,  and a disease node’s size indicates its hub degree  d di d ’ i i di t it h b d (overall number of outbound links). The pale grey zones in the map indicates a high  density of links. The more links a node send to  gene nodes, the bigger it appears on the map.  The diseasome network Diseasome – Mathieu Bastian, Sébastien Heymann Online Information 2009, London
  • How did we create the map? Nodes are positioned on the map according to a topological placement algorithm, i.e. each node is  positioned solely according to its linking pattern. Many softwares are available for doing this. Gephi has  been chosen for its high quality algorithm ForceAtlas. been chosen for its high quality algorithm ForceAtlas From original data, several compatible GEXF graph file have been created. Graphs layouts and rendering  have been performed by Gephi network visualization software. Isolated disorders are not shown and  only the giant component has been ketp. only the giant component has been ketp Many algorithms make possible for a 2D rendering of an adjacent matrix ‐ i.e. the matrix describing any  graph. We used a ForceAtlas algorithm, which shares with all the others the same basic principle:  minimizing the system s energy while maximizing the use of the space available for the representation  minimizing the system’s energy while maximizing the use of the space available for the representation of the data. To minimize the system’s energy, one can for instance assume that nodes that are not linked  to each other are pushing away from each other whereas nodes that are linked to each other are  attracting each other. Through iterative steps the algorithm find a balanced spatial placement of the  inherent structure of the network. These positioning principles call for the following reading conventions: • A node’s position on the map depends solely upon its links. A node has no predefined position,  the latter being the result of the relations it has with other nodes. This means that a node with no links  the latter being the result of the relations it has with other nodes. This means that a node with no links at all cannot be positioned on the map; • North, East, South and West don’t matter. The displayed space is not based on the cardinal system  (North, East, South, West), which means that the choice of a relative left‐right or top‐down position is  p purely arbitrary; y y Diseasome – Mathieu Bastian, Sébastien Heymann Online Information 2009, London
  • Map interactions • Search by gene‐disease name • Zoom in/out • Node selection displaying graphical distinction between inbound and outbound links • Filtering by category of disease • Seeing the distribution of the different categories on the map as a pie chart or a bar chart. Map interface Map interface Diseasome – Mathieu Bastian, Sébastien Heymann Online Information 2009, London
  • Access to online related documents and databases A click on selected node label gives an access to a page aggregating related resources: • original linked data on D2R server • TermSciences, the INIST‐CNRS terminological database for science • MeSH, the Medical Subject Headings vocabulary • Wikipedia Resources  for a disease Map Concept tree on TermSciences Diseasome – Mathieu Bastian, Sébastien Heymann Online Information 2009, London
  • Printable poster The poster share results and enhance collaborative work,  by facilitating discussions  about the data or the view. A hi‐resolution printable PDF is available for  communication and collaborative exploration. Poster Diseasome – Mathieu Bastian, Sébastien Heymann Online Information 2009, London
  • Expert tool Users are able to create their own view on data by  launching the Gephi applet in the browser.  It helps to understand how we did, and proposing graphical alternatives. Gephi is an open source software available at an open source software available http://gephi.org. Gephi software Diseasome – Mathieu Bastian, Sébastien Heymann Online Information 2009, London
  • Usages and goals Usages • Finding diseases « proximity » linked by shared activated genes • Browse the related documents from scientific databases: TermScience, MeSH, OMIM Goals • Propose an alternative user experience • Allowing graphical exploration readings and document discovery • Promoting the book Biologie ‐ L'ère numérique (Biology ‐ The digital era) directed by Magali Roux  (ISBN: 978 2 271 06779 1) (ISBN: 978‐2‐271‐06779‐1) Diseasome – Mathieu Bastian, Sébastien Heymann Online Information 2009, London
  • Benefits for scientific document databases A map is a tool of power, a complex world reduced on a plain surface and an object with shapes a user  can dominate and understand. A main issue remains how to read and interprete them correctly. Benefits • Access to weakly or non‐ordered documents with complex relationships. • Intuitive knowledge discovery. I t iti k l d di • Speed up document searching with graphical signs. “Expedition Zukunft” the German train  presenting the map of science (Kevin W.  Boyack, Katy Börner, & Richard Klavans), 2009 Diseasome – Mathieu Bastian, Sébastien Heymann Online Information 2009, London
  • Perspectives and conclusion Diseasome is an attempt to outline futur designs of scientific information systems. Innovative items • Document relationships design as a way to  hold non‐trivial contexts of research non‐trivial contexts of research. • Graph visualization is currently used to  represent different kind of networks (social,  biological, physical, transports)…and mapping scientific publications publications. • Data with network‐like organization may reveal properties only observable and measurable by a network‐based approach in analysis and  visualization systems. l • Maps allow integrating different kind of data  and dimensions for their exploration and  manipulation. Multi‐level networks, A.L. Barabasi Diseasome – Mathieu Bastian, Sébastien Heymann Online Information 2009, London
  • Diseasome Thank you y Mathieu Bastian, Sebastien Heymann INIST‐CNRS, France December 2009