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Revealing topics and their evolution in biomedical literature using Bio-DTM: a case study of ginseng
BACKGROUND: Valuable scientific results on biomedicine are very rich, but they are widely scattered in the literature. Topic modeling enables researchers to discover themes from an unstructured collection of documents without any prior annotations or labels. In this paper, taking ginseng as an examp...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5596940/ https://www.ncbi.nlm.nih.gov/pubmed/28919923 http://dx.doi.org/10.1186/s13020-017-0148-7 |
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author | Chen, Qian Ai, Ni Liao, Jie Shao, Xin Liu, Yufeng Fan, Xiaohui |
author_facet | Chen, Qian Ai, Ni Liao, Jie Shao, Xin Liu, Yufeng Fan, Xiaohui |
author_sort | Chen, Qian |
collection | PubMed |
description | BACKGROUND: Valuable scientific results on biomedicine are very rich, but they are widely scattered in the literature. Topic modeling enables researchers to discover themes from an unstructured collection of documents without any prior annotations or labels. In this paper, taking ginseng as an example, biological dynamic topic model (Bio-DTM) was proposed to conduct a retrospective study and interpret the temporal evolution of the research of ginseng. METHODS: The system of Bio-DTM mainly includes four components, documents pre-processing, bio-dictionary construction, dynamic topic models, topics analysis and visualization. Scientific articles pertaining to ginseng were retrieved through text mining from PubMed. The bio-dictionary integrates MedTerms medical dictionary, the second edition of side effect resource, a dictionary of biology and HGNC database of human gene names (HGNC). A dynamic topic model, a text mining technique, was used to emphasize on capturing the development trends of topics in a sequentially collected documents. Besides the contents of topics taken on, the evolution of topics was visualized over time using ThemeRiver. RESULTS: From the topic 9, ginseng was used in dietary supplements and complementary and integrative health practices, and became very popular since the early twentieth century. Topic 6 reminded that the planting of ginseng is a major area of research and symbiosis and allelopathy of ginseng became a research hotspot in 2007. In addition, the Bio-DTM model gave an insight into the main pharmacologic effects of ginseng, such as anti-metabolic disorder effect, cardioprotective effect, anti-cancer effect, hepatoprotective effect, anti-thrombotic effect and neuroprotective effect. CONCLUSION: The Bio-DTM model not only discovers what ginseng’s research involving in but also displays how these topics evolving over time. This approach can be applied to the biomedical field to conduct a retrospective study and guide future studies. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13020-017-0148-7) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5596940 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-55969402017-09-15 Revealing topics and their evolution in biomedical literature using Bio-DTM: a case study of ginseng Chen, Qian Ai, Ni Liao, Jie Shao, Xin Liu, Yufeng Fan, Xiaohui Chin Med Research BACKGROUND: Valuable scientific results on biomedicine are very rich, but they are widely scattered in the literature. Topic modeling enables researchers to discover themes from an unstructured collection of documents without any prior annotations or labels. In this paper, taking ginseng as an example, biological dynamic topic model (Bio-DTM) was proposed to conduct a retrospective study and interpret the temporal evolution of the research of ginseng. METHODS: The system of Bio-DTM mainly includes four components, documents pre-processing, bio-dictionary construction, dynamic topic models, topics analysis and visualization. Scientific articles pertaining to ginseng were retrieved through text mining from PubMed. The bio-dictionary integrates MedTerms medical dictionary, the second edition of side effect resource, a dictionary of biology and HGNC database of human gene names (HGNC). A dynamic topic model, a text mining technique, was used to emphasize on capturing the development trends of topics in a sequentially collected documents. Besides the contents of topics taken on, the evolution of topics was visualized over time using ThemeRiver. RESULTS: From the topic 9, ginseng was used in dietary supplements and complementary and integrative health practices, and became very popular since the early twentieth century. Topic 6 reminded that the planting of ginseng is a major area of research and symbiosis and allelopathy of ginseng became a research hotspot in 2007. In addition, the Bio-DTM model gave an insight into the main pharmacologic effects of ginseng, such as anti-metabolic disorder effect, cardioprotective effect, anti-cancer effect, hepatoprotective effect, anti-thrombotic effect and neuroprotective effect. CONCLUSION: The Bio-DTM model not only discovers what ginseng’s research involving in but also displays how these topics evolving over time. This approach can be applied to the biomedical field to conduct a retrospective study and guide future studies. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13020-017-0148-7) contains supplementary material, which is available to authorized users. BioMed Central 2017-09-12 /pmc/articles/PMC5596940/ /pubmed/28919923 http://dx.doi.org/10.1186/s13020-017-0148-7 Text en © The Author(s) 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Chen, Qian Ai, Ni Liao, Jie Shao, Xin Liu, Yufeng Fan, Xiaohui Revealing topics and their evolution in biomedical literature using Bio-DTM: a case study of ginseng |
title | Revealing topics and their evolution in biomedical literature using Bio-DTM: a case study of ginseng |
title_full | Revealing topics and their evolution in biomedical literature using Bio-DTM: a case study of ginseng |
title_fullStr | Revealing topics and their evolution in biomedical literature using Bio-DTM: a case study of ginseng |
title_full_unstemmed | Revealing topics and their evolution in biomedical literature using Bio-DTM: a case study of ginseng |
title_short | Revealing topics and their evolution in biomedical literature using Bio-DTM: a case study of ginseng |
title_sort | revealing topics and their evolution in biomedical literature using bio-dtm: a case study of ginseng |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5596940/ https://www.ncbi.nlm.nih.gov/pubmed/28919923 http://dx.doi.org/10.1186/s13020-017-0148-7 |
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