Diseasome

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

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Diseasome

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