title: A Bibliometric Analysis on Cancer Population Science with Topic Modeling.
abstract: Bibliometric analysis is a research method used in library and information science to evaluate research performance. It applies quantitative and statistical analyses to describe patterns observed in a set of publications and can help identify previous, current, and future research trends or focus. To better guide our institutional strategic plan in cancer population science, we conducted bibliometric analysis on publications of investigators currently funded by either Division of Cancer Preventions (DCP) or Division of Cancer Control and Population Science (DCCPS) at National Cancer Institute. We applied two topic modeling techniques: author topic modeling (AT) and dynamic topic modeling (DTM). Our initial results show that AT can address reasonably the issues related to investigators' research interests, research topic distributions and popularities. In compensation, DTM can address the evolving trend of each topic by displaying the proportion changes of key words, which is consistent with the changes of MeSH headings.
authors: Biomedical Statistics & Informatics, Mayo Clinic, Rochester, MN.
authors: Ding-Cheng
authors: DC
authors: Li
authors: Biomedical Statistics & Informatics, Mayo Clinic, Rochester, MN.
authors: Majid
authors: M
authors: Rastegar-Mojarad
authors: Health Science Research, Mayo Clinic, Scottsdale, AZ.
authors: Janet
authors: J
authors: Okamoto
authors: Biomedical Statistics & Informatics, Mayo Clinic, Rochester, MN.
authors: Hongfang
authors: H
authors: Liu
authors: Health Science Research, Mayo Clinic, Scottsdale, AZ.
authors: Scott
authors: S
authors: Leichow