Browse > Article
http://dx.doi.org/10.5808/GI.2019.17.3.e25

Trends in Genomics & Informatics: a statistical review of publications from 2003 to 2018 focusing on the most-studied genes and document clusters  

Kim, Ji-Hyeon (Bioinformatics Laboratory, ELTEC College of Engineering, Ewha Womans University)
Nam, Hee-Jo (Bioinformatics Laboratory, ELTEC College of Engineering, Ewha Womans University)
Park, Hyun-Seok (Bioinformatics Laboratory, ELTEC College of Engineering, Ewha Womans University)
Abstract
Genomics & Informatics (NLM title abbreviation: Genomics Inform) is the official journal of the Korea Genome Organization. Herein, we conduct a statistical analysis of the publications of Genomics & Informatics over the 16 years since its inception, with a particular focus on issues relating to article categories, word clouds, and the most-studied genes, drawing on recent reviews of the use of word frequencies in journal articles. Trends in the studies published in Genomics & Informatics are discussed both individually and collectively.
Keywords
document clustering; genes; shallow neural network; word cloud;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 Oh SY, Kim JH, Kim SJ, Nam HJ, Park HS. GNI Corpus version 1.0: annotated full-text corpus of Genomics & Informatics to support biomedical information extraction. Genomics Inform 2018;16:75-77.   DOI
2 Biber D, Conrad S, Reppen R. Corpus Linguistics: Investigating Language Structure and Use. Cambridge: Cambridge University Press, 1998.
3 Hiles ID, Otsu M, Volinia S, Fry MJ, Gout I, Dhand R, et al. Phosphatidylinositol 3-kinase: structure and expression of the 110 kd catalytic subunit. Cell 1992;70:419-429.   DOI
4 National Cancer Institute. BRCA1 and BRCA2: Cancer Risk and Genetic Testing. Bethesda: National Cancer Institute, 2014.
5 Dolgin E. The most popular genes in the human genome. Nature 2017;551:427-431.   DOI
6 Forgy E. Cluster analysis of multivariate data: efficiency versus interpretability of classifications. Biometrics 1965;21:768-769.
7 Lloyd S. Least squares quantization in PCM. IEEE Trans Inf Theory 1982;28:129-137.   DOI
8 Leskovec J, Rajaraman A, Ullman JD. Mining of Massive Datasets. Cambridge: Cambridge University Press, 2011.
9 Mikolov T, Chen K, Corrado G, Dean J. Efficient estimation of word representations in vector space. Ithaca: arXiv, Cornell University, 2013. Accessed 2018 Jul 29. Available from: https://arxiv.org/abs/1301.3781.
10 Genomics and Informatics archives. Seoul: Korea Genome Organization, 2018. Accessed 2018 Jul 29. Available from: https://genominfo.org/articles/archive.php.
11 Halvey MJ, Keane MT. An assessment of tag presentation techniques. In: Proceedings of the 16th International Conference on World Wide Web (Williamson C, Zurko ME, Patel-Schneider P, Shenoy P, eds.), 2007 May 8-12, Banff, Alberta, Canada. New York: ACM, 2007. pp. 1313-1134.