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  • × author_ss:"Ding, Y."
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  1. Song, M.; Kim, S.Y.; Zhang, G.; Ding, Y.; Chambers, T.: Productivity and influence in bioinformatics : a bibliometric analysis using PubMed central (2014) 0.01 
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    Abstract
    Bioinformatics is a fast-growing field based on the optimal use of "big data" gathered in genomic, proteomics, and functional genomics research. In this paper, we conduct a comprehensive and in-depth bibliometric analysis of the field of bioinformatics by extracting citation data from PubMed Central full-text. Citation data for the period 2000 to 2011, comprising 20,869 papers with 546,245 citations, was used to evaluate the productivity and influence of this emerging field. Four measures were used to identify productivity; most productive authors, most productive countries, most productive organizations, and most popular subject terms. Research impact was analyzed based on the measures of most cited papers, most cited authors, emerging stars, and leading organizations. Results show the overall trends between the periods 2000 to 2003 and 2004 to 2007 were dissimilar, while trends between the periods 2004 to 2007 and 2008 to 2011 were similar. In addition, the field of bioinformatics has undergone a significant shift, co-evolving with other biomedical disciplines.
  2. Lin, N.; Li, D.; Ding, Y.; He, B.; Qin, Z.; Tang, J.; Li, J.; Dong, T.: ¬The dynamic features of Delicious, Flickr, and YouTube (2012) 0.01 
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    Abstract
    This article investigates the dynamic features of social tagging vocabularies in Delicious, Flickr, and YouTube from 2003 to 2008. Three algorithms are designed to study the macro- and micro-tag growth as well as the dynamics of taggers' activities, respectively. Moreover, we propose a Tagger Tag Resource Latent Dirichlet Allocation (TTR-LDA) model to explore the evolution of topics emerging from those social vocabularies. Our results show that (a) at the macro level, tag growth in all the three tagging systems obeys power law distribution with exponents lower than 1; at the micro level, the tag growth of popular resources in all three tagging systems follows a similar power law distribution; (b) the exponents of tag growth vary in different evolving stages of resources; (c) the growth of number of taggers associated with different popular resources presents a feature of convergence over time; (d) the active level of taggers has a positive correlation with the macro-tag growth of different tagging systems; and (e) some topics evolve into several subtopics over time while others experience relatively stable stages in which their contents do not change much, and certain groups of taggers continue their interests in them.
  3. Ding, Y.: Applying weighted PageRank to author citation networks (2011) 0.01 
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    Date
    22. 1.2011 13:02:21
  4. Ding, Y.; Zhang, G.; Chambers, T.; Song, M.; Wang, X.; Zhai, C.: Content-based citation analysis : the next generation of citation analysis (2014) 0.01 
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    Date
    22. 8.2014 16:52:04

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