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| Identifying “Sleeping Beauties” in Cell Biology and Exploring Their Classical Applications through an Improved BP Network and Function Fitting |
| Hu Zewen, Jin Xinyue, Cui Jingjing |
| School of Management Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044 |
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Abstract Identifying “sleeping beauties” from a large number of studies and recommending them to the scientific community can enable the full use of their scientific and technological value, thus driving the development of science and technology. In this study, we designed and implemented an improved back propagation (BP) neural network model by merging the K-value algorithm, quadratic function fitting method, least squares method, and an iterative algorithm. We then used these methods to identify “sleeping beauties” from 401,130 papers in the field of cell biology, from 1990 to 2010, and explored the classical applications of the identified papers. The results show that: (1) the BP neural network can improve the degree of automation in identifying “sleeping beauties.” However, it is necessary to identify some “sleeping beauties” in advance in a training set to train the recognition model. The improved bivariate quadratic function fitting method and Gini coefficient, based on the least squares method, an iterative algorithm, and a slicing algorithm, demonstrate optimal speed in identifying “sleeping beauties”. (2) The recognition effect of the bivariate quadratic function fitting method is not affected by the length of the citation period. However, the recognition effect of the Gini coefficient is influenced by the length of the citation period. This is illustrated by the fact that the number of identified “sleeping beauties” from papers within a shorter citation period (i.e., published between 2001 and 2010) is 15 times as much as that from papers within a longer citation period (i.e., published between 1990 and 2000). (3) In the same field, there is a difference in the number of “sleeping beauties” identified using different methods. As an illustration, among 257,562 papers, the K-value algorithm, BP neural network model, and quadratic function fitting method with optimal recognition effect can identify between 30 and 223 “sleeping beauties”, an identification percentage that is less than 0.09%. The Gini coefficient with a poorer recognition effect is influenced by the length of the citation period, and identifies a maximum of 1066 “sleeping beauties”; i.e., the percentage increases to 0.41%. (4) The annual distribution of the number of identified “sleeping beauties” is maintained at a stable percentage between 0.02% and 0.17%. (5) The mechanism of identifying “sleeping beauties” can be widely applied in the comparison and analysis of bibliometric features among different types of literatures, as well as the recognition and recommendation of research hotspots. Moreover, the value of the contents of such excellent papers identified as “sleeping beauties” could then be realized.
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Received: 25 April 2022
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