Scientometrics analysis to investigate the potency of biomedicine research based on Indonesia biodiversity publication

##plugins.themes.bootstrap3.article.main##

IRENE MUFLIKH NADHIROH
RIA HARDIYATI
MIA AMELIA
RIZKA RAHMAIDA
TRI HANDAYANI

Abstract

Nadhiroh IM, Hardiyati R, Amelia M, Rahmaida R, Handayani T. 2017. Scientometrics analysis to investigate the potency of biomedicine research based on Indonesia biodiversity publication. Pros Sem Nas Masy Biodiv Indon 3: 345-350. Indonesia as a mega biodiversity country, has great potential with a wealth of biodiversity. One of them is the discovery of biomedical drugs derived from genetic resources originating from Indonesia. Research related to genetic resources of biodiversity in Indonesia has been done a lot. Some of them are related to new drug discovery process based on genetic resources of Indonesia's biodiversity. Scientometrics is a quantitative approach in research on the development of science and technology. This study aims to analyze the potential discovery of biomedical drugs based on scientific publications using the approach Scientometrics. One application of saintometrika is to reveal the potential and scientific trends of scientific publication data. Many methods can be used related to the application, one of which is Coword analysis method. The data used in this study is the international scientific publication data biomedical Indonesia related biomedical data originating from Scopus. Based on these data, the majority of research stages conducted are still at drug discovery stage. Therefore, further analysis needs to be needed to provide guidance in conducting more efficient research. The results indicate the great potential of some species of biodiversity in Indonesia, judging by their relation to keywords related to drug discovery, such as anti-cancer, antioxidant, and others. This result can be applied in research management, ie by providing more funds in the study, so as to produce useful products.

##plugins.themes.bootstrap3.article.details##

References
Blagosklonny MV. 2002. P53: an ubiquitous target of anticancer drugs.
Intl J Cancer 98 (2): 161-166.

Chaman Sab M, Dharani Kumar P, Biradar BS. 2017. Mapping of Indian
biomedicine Research: A Scientometric Analysis of Research Output
During 2012 -2016. Indian J Inform Sour Serv 7 (2): 2231-6094.

Handayani TH, Amelia M, Rahmaida R, Hardiyati R, Nadhiroh IM. 2016.
Kajian Saintometrika Perkembangan Publikasi Ilmiah
Keanekaragaman Hayati Indonesia sebagai Bahan Rekomendasi
Kebijakan Arah Penelitian Keanekaragaman Hayati Nasional.
Laporan Teknis Penelitian. PAPPIPTEK-LIPI.

Jeong S, Kim H.-G. 2010. Intellectual structure of biomedical informatics
reflected in scholarly events. Scientometrics 85 (2): 541-551.

Lakitan B, Hidayat D, Herlinda S. 2012. Scientific productivity and the
collaboration intensity of Indonesian universities and public R&D
institutions: Are there dependencies on collaborative R&D with
foreign institutions? Technol Soc 34 (3): 227-238.

Lee S, Bozeman B. 2005. The Impact of Research Collaboration on
Scientific Productivity. Soc Stud Sci 35 (5): 673-702.

Lee S, Yoon B, Lee C, Park J. 2009a. Business planning based on
technological capabilities: Patent analysis for technology-driven
roadmapping. Technol Forecast Soc Change 76 (6): 769-786.

Lee S, Yoon B, Park Y. 2009b. An approach to discovering new
technology opportunities: Keyword-based patent map approach.
Technovation 29 (6-7): 481-497.

Lievrouw LA, Rogers EM, Lowe CU, Nadel E. 1987. Triangulation as a
research strategy for identifying invisible colleges among biomedical
scientists. Social Networks 9 (3): 217-248.

Liu X, Zhang L, Hong S. 2011. Global biodiversity research during 1900–
2009: a bibliometric analysis. Biodiv Conserv 20 (4): 807–826.

Perez-Iratxeta C, Bork P, Andrade MA. 2002. Association of genes to
genetically inherited diseases using data mining. Nature Genet 31 (3):
316-319.

Rebholz-Schuhmann D, Grabmü Ller C, Kavaliauskas S, Croset S,
Woollard P, Backofen R et al. 2013. A case study: semantic
integration of gene-disease associations for type 2 diabetes mellitus
from literature and biomedical data resources. Drug Discovery Today
0 (0). DOI: 10.1016/j.drudis.2013.10.024

Rebholz-Schuhmann D, Oellrich A, Hoehndorf R. 2012. Text-mining
solutions for biomedical research: enabling integrative biology.
Nature Rev Genet 13 (12): 829-839.

Zhu F, Patumcharoenpol P, Zhang C et al. 2013. Biomedical text mining
and its applications in cancer research. J Biomed Inform 46 (2): 200-
211.