Journal Articles

  • Lou, W., He, J., Zhang, L., Zhu, Z., & Zhu, Y. (2023). Support behind the scenes: The relationship between acknowledgement, coauthor, and citation in Nobel articles. Scientometrics

  • Hao, J., Zhang, P., Che, C., Jin, B., & Zhu, Y. (2023). CariesFG: A fine-grained RGB image classification framework with attention mechanism for dental caries. Engineering Applications of Artificial Intelligence

  • Zhang, P., Chen, J., Che, C., Zhang, L., Jin, B., & Zhu, Y. (2023). IEA-GNN: Anchor-aware Graph Neural Network Fused with Information Entropy for Node Classification and Link Prediction. Information Sciences

  • Kim, D., Quan, L., Seo, M., Kim, K., Kim, J.W., & Zhu, Y. (2023). Interpretable machine learning-based approaches for understanding suicide risk and protective factors among South Korean females using survey and social media data. Suicide and Life-Threatening Behavior

  • Kim, D., Jung, W., Jiang, T., & Zhu, Y. (2023). An Exploratory Study of Medical Journal’s Twitter Use: Metadata, Networks, and Content Analyses. Journal of Medical Internet Research,25:e43521

  • Lou, W., He, J., Xu, Q., Zhu, Z., Lu, Q., & Zhu, Y. (2023). Rhetorical structure parallels research topic in LIS articles: a temporal bibliometrics examination. Aslib Journal of Information Management

  • Zhu, Y., Quan, L., Chen, P-Y., Kim, M.C., & Che, C. (2023). Predicting coauthorship using bibliographic network embedding. Journal of the Association for Information Science & Technology, 74(4), 388-401

  • Oh, H., Nam, S., & Zhu, Y. (2023). Structured Abstract Summarization of Scientific Articles: Summarization Using Full-text Section Information. Journal of the Association for Information Science & Technology, 74(2), 234-248

  • Zhu, Y., Nam, S., Quan, L., Baek, J., Jeon, H., & Tang, B. (2022). Linking Suicide and Social Determinants of Health in South Korea: An Investigation of Structural Determinants. Frontiers in Public Health, 10:1022790

  • Liu, Y., Zhong, Z., Che, C., & Zhu, Y. (2022). Recommendations with residual connections and negative sampling based on knowledge graphs. Knowledge-Based Systems, 258

  • Shan, Y., Che, C., Wei, X., Wang, X., Zhu, Y., & Jin, B.  (2022). Bi-graph attention network for aspect category sentiment classification. Knowledge-Based Systems, 258

  • Nam, S., Kim, D., Jung, W., & Zhu, Y.(2022). Understanding the Research Landscape of Deep Learning in Biomedical Science: Scientometric Analysis. Journal of Medical Internet Research, 24(4):e28114

  • Kim, D., Jung, W., Nam, S., Jeon, H., Baek, J., & Zhu, Y.(2022). Understanding information behavior of South Korean Twitter users who express suicidality on Twitter. Digital Health. 8

  • Jung, W., Kim, D., Nam, S., & Zhu, Y.(2021). Suicidality detection on social media using metadata and text feature extraction and machine learning. Archives of Suicide Research.

  • Wu, C., Yan, E., Zhu, Y., & Li, K. (2021). Gender imbalance in the productivity of funded projects: A study of the outputs of National Institutes of Health R01 grants. Journal of the Association for Information Science & Technology. 72(11), 1386-1399.

  • Kim, M., Feng, Y., & Zhu, Y. (2021). Mapping scientific profile and knowledge diffusion of Library Hi Tech. Library Hi Tech, 39(2), 549-573.

  • Zhu, Y., Kim, D., Yan, E., Kim, M. C., & Qi, G. (2021). Analyzing China’s research collaboration with the United States in high-impact and high-technology research. Quantitative Science Studies, 2(1), 363-375.

  • Yan, E., Zhu, Y., & He, J. (2020). Analyzing academic mobility of US professors based on ORCID data and the Carnegie Classification. Quantitative Science Studies, 1(4), 1451-1467.

  • Kim, M., Nam, S., Wang, F., & Zhu, Y. (2020). Mapping scientific landscapes in UMLS research: a scientometric review. Journal of the American Medical Informatics Association, 27(10), 1612-1624

  • Zhu, Y., Che, C., Jin, B., Zhang, N., Su, C., & Wang, F. (2020). Knowledge-driven drug repurposing using a comprehensive drug knowledge graph. Health Informatics Journal, 26(4), 2737-2750.

  • Zhu, Y., Jung, W., Wang, F., & Che, C. (2020). Drug repurposing against Parkinson’s disease by text mining the scientific literature. Library Hi Tech, 38(4), 741-750.

  • Zhu, Y., Yan, E., Peroni, S., & Che, C. (2020). Nine million book items and eleven million citations: a study of book-based scholarly communication using OpenCitations. Scientometrics, 122(2), 1097-1112.

  • Su, C., Tong, J., Zhu, Y., Cui, P., & Wang, F. (2020). Network embedding in biomedical data science. Briefings in Bioinformatics, 21(1), 182-197.

  • Zhu, Y., Olivier, E., Pathak, J., & Wang, F. (2019). Drug knowledge bases and their applications in biomedical informatics research. Briefings in Bioinformatics, 20(4), 1308-1321.

  • Kim, M.H., Banerjee, S., Zhao, Y., Wang, F., Zhang, Y., Zhu, Y., DeFerio, J., Evans, L., Park, S.M., & Pathak, J. (2018). Association networks in a matched case-control design – Co-occurrence patterns of preexisting chronic medical conditions in patients with major depression versus their matched controls. Journal of Biomedical Informatics, 87, 88-95.

  • Zhang, F., Yan, E., Niu, X., & Zhu, Y. (2018) Joint modeling of the association between NIH funding and its three primary outcomes: patents, publications, and citation impact. Scientometrics, 117(1), 591-602.

  • Zhu, Y., Kim, M., Banerjee, S., Deferio, J., Alexopoulos, G.S., & Pathak, J. (2018). Understanding the research landscape of major depressive disorder via literature mining: an entity-level analysis of PubMed data from 1948-2017. JAMIA OPEN, 1(1), 115–121

  • Yan, E. & Zhu, Y. (2018). Tracking word semantic change in biomedical literature. International Journal of Medical Informatics, 109, 76-86.

  • Song, I.-Y. & Zhu, Y. (2017). Big Data and Data Science: Opportunities and Challenges of iSchools. Journal of Data and Information Science, 2(3), 1-18.

  • Zhu, Y., Yan, E., & Song, I.-Y. (2017). A natural language interface to a graph-based bibliographic information retrieval system. Data & Knowledge Engineering, 111, 73-89.

  • Zhu, Y., Yan, E., & Wang, F. (2017). Semantic relatedness and similarity of biomedical terms: examining the effects of recency, size, and section of biomedical publications on the performance of word2vec. BMC Medical Informatics and Decision Making, 17(1), 95.

  • Zhu, Y. & Yan, E. (2017). Examining academic ranking and inequality in library and information science through faculty hiring networks. Journal of Informetrics, 11(2), 641-654.

  • Yan, E. & Zhu, Y. (2017). Adding the dimension of knowledge trading to source impact assessment: Approaches, indicators, and implications. Journal of the Association for Information Science & Technology, 68(5), 1090-1104.

  • Zhu, Y., Kim, M.C., & Chen, C. (2017). An investigation of the intellectual structure of opinion mining research. Information Research, 22(1), paper 739.

  • Zhu, Y. & Yan, E. (2016). Searching bibliographic data using graphs: A visual graph query interface. Journal of Informetrics, 10(4), 1092-1107.

  • Choi, N., Song, I.-Y., & Zhu, Y. (2016). A Model-based Method for Information Alignment: A Case Study on Educational Standards. Journal of Computing Science and Engineering, 10(3), 85-94.

  • Zhu, Y., Yan, E., & Song, M. (2016). Understanding the evolving academic landscape of library and information science through faculty hiring data. Scientometrics, 108(3), 1461-1478.

  • Zhu, Y., Song, M., & Yan, E. (2016). Identifying Liver Cancer and Its Relations with Diseases, Drugs, and Genes: A Literature-based Approach. PLoS ONE, 11(5), e0156091.

  • Zhu, Y., Yan, E. & Song, I.-Y. (2016). The use of a graph-based system to improve bibliographic information retrieval: System design, implementation, and evaluation. Journal of the Association for Information Science & Technology, 68(2), 480-490.

  • Kim, M.C., Zhu, Y., & Chen, C. (2016). How are they different? A quantitative domain comparison of information visualization and data visualization (2000-2014). Scientometrics, 107(1), 123-165.

  • Song, I.-Y. & Zhu, Y. (2015). Big data and data science: what should we teach? Expert Systems, 33(4), 364-373.

  • Yan, E. & Zhu, Y. (2015). Identifying entities from scientific publications: A comparison of vocabulary- and model-based methods. Journal of Informetrics, 9(3), 455–465.

  • Zhu, Y. & Yan, E. (2015). Dynamic subfield analysis of disciplines: An examination of the trading impact and knowledge diffusion patterns of computer science. Scientometrics, 104(1), 335-359.

  • Kim, H., Zhu, Y., Kim, W., & Sun, T. (2014). Dynamic faceted navigation in decision making using Semantic Web technology. Decision Support Systems, 61, 59-68.

Conference Papers

  • Che, C., Zhu, M., Zhu, Y., Zhang, Q., Zhou. D., & Wang. B. (2020) A Protein Embedding Model for Drug Molecular Screening. IEEE BigComp 2020, Busan, Korea.
  • Kim, J., Kim, J., & Zhu, Y. (2019) Analyzing public opinion toward the 2019 North Korea–United States summit through mining twitter. ASIS&T 2019, Melbourne, Australia.
  • Yun, J. & Zhu, Y. (2019) An analysis of physical characteristics of Joseon Dynasty books using statistical approaches. ASIS&T 2019, Melbourne, Australia.
  • Kim, J., Koo, Y., & Zhu, Y. (2019) A study for categorizing relations between headword and aliases. ASIS&T 2019, Melbourne, Australia.
  • Zhu, Y., Kim, M.C., & Yan, E. (2018) Evaluating interactive bibliographic information retrieval systems: A user-centered approach. ASIS&T 2018, Vancouver, Canada.
  • Kim, M.H., Zhu, Y., Banerjee, S., Evans, L., Zhang, Y., Wang, F., Park, S.M., & Pathak, J. (2018) Comparing sex-specific association networks of chronic medical conditions. IEEE ICHI 2018. New York City, USA.
  • Yan, E. & Zhu, Y. (2017). Word semantic change: The law of differentiation vs. the law of parallel change. ISSI 2017. Wuhan, China.
  • Song, I.-Y., Zhu, Y., Ceong, H., & Thonggoom, O. (2015). Methodologies for Semi-automated Conceptual Data Modeling from Requirements. ER 2015. Stockholm, Sweden.
  • Zhu, Y., Yan, E., & Song, I.-Y. (2015). Topological Analysis of Interdisciplinary Scientific Journals: Which Journals Will be the Next Nature or Science? ACM RACS 2015. Prague, Czech Republic.
  • Kim, M. C., Feng, Y., Zhu, Y., & Ping, Q. (2015). Quantitative exploration into the diffusion process of creative ideas. ASIS&T 2015. Missouri, USA.
  • Zhu, Y., Jeon, D., Kim, W., Hong, J. S., Lee, M., Wen, Z., & Cai, Y. (2012). The Dynamic Generation of Refining Categories in Ontology-Based Search. JIST 2012. Nara, Japan.

Book Chapters

  • Kim, M.C. & Zhu, Y. (2018) Scientometrics of Scientometrics: Mapping Historical Footprint and Emerging Technologies in Scientometrics. In Scientometrics. IntechOpen