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Helping Researchers Cope With the Medical Literature Knowledge Explosion

By KurzweilAI.net

August 27, 2014

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Computational biologists at the Baylor College of Medicine and analytics experts at IBM Research have collaborated to create a new tool called the Knowledge Integration Toolkit (KnIT), which will help research scientists to make better use of the massive volumes of scientific research that is available in public databases.

KnIT relies in part on IBM's Watson supercomputer to help mine public databases for relevant studies and then quickly digest and synthesize them so they can be used to formulate new hypotheses.

Olivier Lichtarge, director of Baylor's Center of Computational and Integrative Biomedical Research, says that especially in biology and medicine there is a tremendous amount of research that no single scientist could ever hope to completely absorb in a timely fashion, and the volume of research is always increasing. Lichtarge uses the example of a single tumor-suppressing protein, p53, which is discussed in some 70,000 research papers. Reading them all would take a researcher decades, but KnIT is able to do the job exponentially faster.

In a test using the literature for p53, KnIT was able to generate a hypothesis about the protein that was later proved to be accurate.

Lichtarge will discuss the study and the KnIT project today at the ACM SIGKDD Conference on Knowledge Discovery and Data Mining.

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