Description

This tutorial presents novel computational analytic methods capable of unlocking the human knowledge that’s been documented and archived in the unstructured text of hundreds of millions of scientific publications to extend scientific discovery beyond human capacity, and ways to automate experimental knowledge generation. The instructors will explore pathways for visualizing and comprehending knowledge propagation, evolution, and assessment of scientific research fronts, data-based hypothesis generation and methods for quantifying research impact within the scientific community and beyond.

In Part One of this three-part tutorial, Ichiro Takeuchi discussess the use of informatics techniques to effectively handle, visualize and analyze the large amount of data that are generated from the combinatorial experiments and potential of data mining of publications to establish knowledge-driven research paradigms. In addition to use of multivariate statistical analysis and machine learning techniques, the need for text-based knowledge extraction for further progress is briefly discussed.

This is Part One of a three-part tutorial.

• Part Two
• Part Three

Speaker(s):

• Ichiro Takeuchi, University of Maryland