Identifying Transformative Research in Biomedical Sciences - Technologies and Applications of Artificial Intelligence - page 195

Figure 4, the cascade disruption can be observed for the cases of transformative
research immediately (our time stamp is a year) after their publication but not
for control papers published in the same year.
4 Conclusions and Discussions
Contributions of the research reported in this paper include that we developed
a framework for identifying successful transformative research by analyzing ci-
tation patterns of scientific publications and that we developed computational
scalable algorithms to analyze massive citation records. Our framework that
considers a paradigm shift as information disruption will enable quantification,
comparison, and characterization of paradigm shifts and transformative research.
The foundation of the framework is a function that characterizes cascade struc-
tures. Exploring this function allows us to develop e cient and scalable algo-
rithms that implement the proposed framework. We have presented encouraging
results here and we will build on the success and complete the development of a
theory of transformative research as our future work.
Acknowledgments. The project is supported in part by grant 100-2221-E-014-
MY3 from the National Science Council, Taiwan, to YHH and MTK and the
Big Data to Knowledge (BD2K) Data Discovery Index Coordination Consortium
(BioCaddie) from the National Institute of Health, USA, to CNH. The authors
would like to thank Professor Y.-T. Liu at the Moores Cancer Center, UC San
Diego, for his comments and help with the early draft of this manuscript.
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