Trends in genomics & informatics: A statistical review of publications from 2003 to 2018 focusing on the most-studied genes and document clusters

Ji Hyeon Kim; Hee Jo Nam; Hyun Seok Park

doi: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

Ji Hyeon Kim, Hee Jo Nam, Hyun Seok Park

Department of Computer Science & Engineering

Research output: Contribution to journal › Short survey › peer-review

1 Scopus citations

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.

Original language	English
Article number	e25
Journal	Genomics and Informatics
Volume	17
Issue number	3
DOIs	https://doi.org/10.5808/GI.2019.17.3.e25
State	Published - 2019

Bibliographical note

Publisher Copyright:
© 2019, Korea Genome Organization.

Keywords

Document clustering
Genes
Shallow neural network
Word cloud

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10.5808/GI.2019.17.3.e25

Cite this

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title = "Trends in genomics \& informatics: A statistical review of publications from 2003 to 2018 focusing on the most-studied genes and document clusters",

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",

author = "Kim, \{Ji Hyeon\} and Nam, \{Hee Jo\} and Park, \{Hyun Seok\}",

note = "Publisher Copyright: {\textcopyright} 2019, Korea Genome Organization.",

year = "2019",

doi = "10.5808/GI.2019.17.3.e25",

language = "English",

volume = "17",

journal = "Genomics and Informatics",

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AU - Nam, Hee Jo

AU - Park, Hyun Seok

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

KW - Document clustering

KW - Genes

KW - Shallow neural network

KW - Word cloud

UR - https://www.scopus.com/pages/publications/85079516874

U2 - 10.5808/GI.2019.17.3.e25

DO - 10.5808/GI.2019.17.3.e25

M3 - Short survey

AN - SCOPUS:85079516874

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VL - 17

JO - Genomics and Informatics

JF - Genomics and Informatics

IS - 3

M1 - e25

ER -

Trends in genomics & informatics: A statistical review of publications from 2003 to 2018 focusing on the most-studied genes and document clusters

Abstract

Bibliographical note

Keywords

Access to Document

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Cite this