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Accelerating Discovery: Mining Unstructured Information for - download pdf or read online

By Scott Spangler

ISBN-10: 1482239132

ISBN-13: 9781482239133

Unstructured Mining ways to resolve advanced medical Problems

As the quantity of medical facts and literature raises exponentially, scientists want extra strong instruments and strategies to approach and synthesize details and to formulate new hypotheses which are probably to be either precise and demanding. Accelerating Discovery: Mining Unstructured info for speculation Generation describes a unique method of medical learn that makes use of unstructured info research as a generative instrument for brand spanking new hypotheses.

The writer develops a scientific procedure for leveraging heterogeneous dependent and unstructured information assets, information mining, and computational architectures to make the invention approach swifter and greater. This strategy hurries up human creativity by means of permitting scientists and inventors to extra comfortably learn and understand the distance of percentages, examine choices, and detect solely new approaches.

Encompassing systematic and sensible views, the ebook offers the required motivation and methods in addition to a heterogeneous set of finished, illustrative examples. It finds the significance of heterogeneous information analytics in assisting medical discoveries and furthers facts technology as a discipline.

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Extra resources for Accelerating Discovery: Mining Unstructured Information for Hypothesis Generation

Example text

Computers can do computation and information retrieval, but scientific discovery requires creativity and thinking “outside the box,” which is just what computers cannot do. A few years ago, the authors would have been largely in agreement with this viewpoint, but something has changed in the field of computer science that makes us believe that accelerating scientific discovery is no longer a distant dream but is actually well within current capability. Later in this chapter, we will describe these recent developments and preview some of the implications of these emerging capabilities.

Discovery will be extremely limited in scope, and wrong conclusions could be drawn. This is often the case with today’s bioinformatics tools, which operate on small and narrowly scoped data sets. We differentiate domain knowledge and domain content deliberately here since they mean different things. Domain knowledge means prior knowledge that has been captured digitally, such as manually curated domain ontologies, taxonomies, dictionaries, and manually curated structured databases. For example, in drug discovery, such domain knowledge may include ChemBL database [7], OBO ontologies [8], and other dictionaries.

Later in this chapter, we will describe these recent developments and preview some of the implications of these emerging capabilities. WHY ACCELERATE DISCOVERY: THE BUSINESS PERSPECTIVE Discovery is central and critical to the whole of humanity and to many of the world’s most significant challenges. Discovery represents an ability to uncover things that are not previously known. 2). Looking at what we human beings consume—for example, consumer goods such as food, clothing, household items, and energy—we would quickly realize that we need significant innovations across the board.

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Accelerating Discovery: Mining Unstructured Information for Hypothesis Generation by Scott Spangler

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