Tools for the text mining-based visualisation of the provenance of biochemical networks

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Systems biology is concerned with the modelling, visualisation and analysis of biochemical networks in which, for instance, metabolites are 'linked' by arrows representing the enzymes which turn one molecule into another or which are modified by particular substances. However, these diagrams are divorced from the scientific evidence on which they are based, which is represented by the scientific literature (and increasingly by online databases). However, the historical scientific literature is huge, and is increasing at an enormous rate (several thousand papers per week) so no one can possibly read it all. One solution is to use computers to 'read' these papers and present to the user only those which carry relevant information. Aspects of this subject are variously known as Natural Language Processing and Text Mining. What Text Mining does is to go through papers, extract the relevant pieces of information from each paper, and present them to the biological reader. A particular problem is the use by biologists of multiple names for the same thing. Text Mining can assist here since it is able to find all the variations of the same name and link them with the relevant text and databases. Text mining can also find the TYPES of relationship between these names, and this is the basis by which computers can discover and display scientific evidence. The Text Mining System will produce and index such evidence, for specific problems, and this will be stored in an appropriately structured database. The aim of the project is therefore to develop and deploy the necessary Text Mining tools and to use them to display the different relationships to the user and the literature on which they are based. This will be done by encoding the interactions using arrows of various colours that will link to a dynamic website of relevant literature that will thus give a direct linkage between the systems biology diagrams and the evidence for them.