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It s one of those days to be recalled often, to bring peace and calm to the soul Whenever I m in an especially stressful situation, I try to recall a happy experience and relive it in my mind Often I ve used the memory of a picnic on the Maine coast with Susan and Keith to calm myself I picture how we stood in the ocean waters and let the waves come to us We were like children, laughing, falling, and feeling the sand shift under our feet I remember the CAT scan I underwent when I was first diagnosed for Parkinson s, and the claustrophobia I felt as my upper.



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Probability is the branch of mathematics which is concerned with the likelihood that events will occur. It is an invaluable tool for scientists since an event is often not guaranteed to occur, but can only be characterized by the fact that it will occur an average number of times given a number of trials. This is also of particular importance to bioinformaticians as the interactions and reactions that occur in biological processes can often only be characterized by probability theory. Probability theory can also be useful in determining the underlying structure of datasets or sequences. This chapter rstly provides a short background to probability and then details a number of the probabilistic approaches to bioinformatics problems. Conditional probability is different from frequentist probability. For example, if 55 per cent of the students in a department are female and 45 per cent are male, then the event: a randomly selected student is female has a probability, according to the frequentist approach, of 0.55, where the probability ranges from 0 (impossible) to 1 (necessary). Such a probability measure re ects data that has already been collected, and a frequentist approach works provided there is an accurate record of the gender of all students. However, in some cases this may not be possible because the data does not exist, or the accuracy of the data cannot be relied upon. In such cases we have to reason under conditions of uncertainty, where we may need to allocate provisional measures of belief in certain facts before we can draw conclusions. For instance, if an





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The basic mechanics of this refactoring are to convert a calling style that has separate data elements and actions that are related to each other so that, instead, a single XML document is created or received. The RPC approach is a traditional method using a synchronous invocation style with an action, parameters, and a return type. The Document approach is more of an asynchronous, message-passing approach, which uses bulk operations to generate a document (for example, out of a database or Java instance) and conversely to receive and handle whole documents in a coarser-grained fashion. The following list describes the various steps for refactoring an RPC-style approach to a document style: 1. Define an XML schema for the document. The first change is to establish and use an external document schema to refer to the document (instead of an included type definition within the WSDL: Define XML Schema for Document). This needs to include all the attributes that were being passed as parameters. Publish the XML schema to a network-accessible location. 2. Make WSDL changes. Then, a few specific changes are needed to the Web Service definition to define a document-oriented, one-way invocation: Be sure to include a namespace declaration for the external XML schema representing the document, then change the message element so that it indicates a body part, and set the element attribute to the type of the root type within the XML

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