Journal:Analyzing the field of bioinformatics with the multi-faceted topic modeling technique
|Full article title||Analyzing the field of bioinformatics with the multi-faceted topic modeling technique|
|Author(s)||Heo, Go Eun; Kang, Keun Young; Song, Min; Lee, Jeong-Hoon|
|Author affiliation(s)||Yonsei University, POSTECH|
|Primary contact||Email: min dot song at yonsei dot ac dot kr|
|Volume and issue||18 (Suppl 7)|
|Distribution license||Creative Commons Attribution 4.0 International|
|This article should not be considered complete until this message box has been removed. This is a work in progress.|
Background: Bioinformatics is an interdisciplinary field at the intersection of molecular biology and computing technology. To characterize the field as a convergent domain, researchers have used bibliometrics, augmented with text-mining techniques for content analysis. In previous studies, Latent Dirichlet Allocation (LDA) was the most representative topic modeling technique for identifying topic structure of subject areas. However, as opposed to revealing the topic structure in relation to metadata such as authors, publication date, and journals, LDA only displays the simple topic structure.
Methods: In this paper, we adopt the Author-Conference-Topic (ACT) model of Tang et al. to study the field of bioinformatics from the perspective of keyphrases, authors, and journals. The ACT model is capable of incorporating the paper, author, and conference into the topic distribution simultaneously. To obtain more meaningful results, we used journals and keyphrases instead of conferences and the bag-of-words. For analysis, we used PubMed to collect forty-six bioinformatics journals from the MEDLINE database. We conducted time series topic analysis over four periods from 1996 to 2015 to further examine the interdisciplinary nature of bioinformatics.
Results: We analyzed the ACT Model results in each period. Additionally, for further integrated analysis, we conducted a time series analysis among the top-ranked keyphrases, journals, and authors according to their frequency. We also examined the patterns in the top journals by simultaneously identifying the topical probability in each period, as well as the top authors and keyphrases. The results indicate that in recent years diversified topics have become more prevalent, and convergent topics have become more clearly represented.
Conclusion: The results of our analysis imply that over time the field of bioinformatics becomes more interdisciplinary where there is a steady increase in peripheral fields such as conceptual, mathematical, and system biology. These results are confirmed by integrated analysis of topic distribution as well as top ranked keyphrases, authors, and journals.
Keywords: bioinformatics, text mining, topic modeling, ACT model, keyphrase extraction
Over the years, academic subject areas have converged to form a variety of new, interdisciplinary fields. Bioinformatics is one example. Research domains from molecular biology to machine learning are used in conjunction to better understand complex biological systems such as cells, tissues, and the human body. Due to the complexity and broadness of the field, bibliometric analysis is often adopted to assess the current knowledge structure of a subject area, specify the current research themes, and identify the core literature of that area.
Bibliometrics identifies research trends using quantitative measures such as a researcher’s number of publications and citations, journal impact factors, and other indices that can measure impact or productivity of author or journal. In addition, other factors such as the affiliation of authors, collaborations, and citation data are often incorporated into bibliometric analysis.
Previous studies mainly rely on quantitative measures and suffer from the lack of content analysis. To incorporate content analysis into bibliometrics, text-mining techniques are applied. Topic-modeling techniques are mostly adopted to identify the topics of a subject area while analyzing that area more abundantly. These techniques allow for enriched content analysis. As an extension of Latent Dirichlet Allocation (LDA), which is the best received topic-modeling technique, Steyvers et al. proposed the author-topic modeling technique that analyzes authors and topics simultaneously. They identify the authors’ impact or productivity of researchers in a given subject area. By adding multiple conditions to LDA, Tang et al. suggested a new methodology, called the Author-Conference-Topic (ACT) model that analyzes the author, conference, and topic in one model to understand the subject area in an integrated manner.
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