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1. Analysis of Persian Bioinformatics Research with Topic Modeling.

Author

Ebrahimi, Fezzeh, Dehghani, Mohammad, and Makkizadeh, Fatemah

Subjects
LIFE sciences, RESEARCH, BIOMARKERS, PHONOLOGICAL awareness, MATHEMATICAL models, NATURAL language processing, RESEARCH methodology, BIBLIOMETRICS, MOLECULAR models, BIOINFORMATICS, MATHEMATICS, CITATION analysis, GENE expression, THEORY, MEDICAL research, INFORMATION technology, ALGORITHMS
Abstract

Purpose. As a scientific field, bioinformatics has drawn remarkable attention from various fields, such as information technology, mathematics, and modern biological sciences, in recent years. The topic models originating from the field of natural language processing have become the focus of attention with the rapid accumulation of biological datasets. Thus, this research is aimed at modeling the topic content of the bioinformatics literature presented by Iranian researchers in the Scopus Citation Database. Methodology. This research was a descriptive-exploratory study, and the studied population included 3899 papers indexed in the Scopus database, which had been indexed in this database until March 9, 2022. The topic modeling was then performed on the abstracts and titles of the papers. A combination of LDA and TF-IDF was utilized for topic modeling. Findings. The data analysis with topic modeling resulted in identifying seven main topics "Molecular Modeling," "Gene Expression," "Biomarker," "Coronavirus," "Immunoinformatics," "Cancer Bioinformatics," and "Systems Biology." Moreover, "Systems Biology" and "Coronavirus" had the largest and smallest clusters, respectively. Conclusion. The present investigation demonstrated an acceptable performance for the LDA algorithm in classifying the topics included in this field. The extracted topic clusters indicated excellent consistency and topic connection with each other. [ABSTRACT FROM AUTHOR]

Published
2023
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2. High-impact and transformative science (HITS) metrics: Definition, exemplification, and comparison

Author

Joseph Staudt, Katy Börner, Robert P. Light, Bruce A. Weinberg, Gerald Marschke, and Huifeng Yu

Subjects
Biomedical Research, Computer science, lcsh:Medicine, Biochemistry, Exemplification, Mathematical and Statistical Techniques, Animal Cells, Multidisciplinary approach, Citation analysis, 050207 economics, lcsh:Science, DNA methylation, Multidisciplinary, Stem Cells, 05 social sciences, Genomics, Animal Models, Research Assessment, Chromatin, Nucleic acids, Experimental Organism Systems, Scientific method, Citation Analysis, Physical Sciences, Epigenetics, Cellular Types, Journal Impact Factor, Periodicals as Topic, DNA modification, Factor Analysis, Statistics (Mathematics), Chromatin modification, Algorithms, Research Article, Chromosome biology, Science, Mouse Models, Bibliometrics, Research and Analysis Methods, 050905 science studies, Human Genomics, Model Organisms, 0502 economics and business, Genetics, Humans, Statistical Methods, Set (psychology), Publishing, lcsh:R, Science Citation Index, Biology and Life Sciences, Computational Biology, Reproducibility of Results, Cell Biology, DNA, Data science, Transformative learning, Obsolescence, lcsh:Q, Gene expression, 0509 other social sciences, Citation, Mathematics
Abstract

Countries, research institutions, and scholars are interested in identifying and promoting high-impact and transformative scientific research. This paper presents a novel set of text- and citation-based metrics that can be used to identify high-impact and transformative works. The 11 metrics can be grouped into seven types: Radical-Generative, Radical-Destructive, Risky, Multidisciplinary, Wide Impact, Growing Impact, and Impact (overall). The metrics are exemplified, validated, and compared using a set of 10,778,696 MEDLINE articles matched to the Science Citation Index ExpandedTM. Articles are grouped into six 5-year periods (spanning 1983-2012) using publication year and into 6,159 fields constructed using comparable MeSH terms, with which each article is tagged. The analysis is conducted at the level of a field-period pair, of which 15,051 have articles and are used in this study. A factor analysis shows that transformativeness and impact are positively related (ρ = .402), but represent distinct phenomena. Looking at the subcomponents of transformativeness, there is no evidence that transformative work is adopted slowly or that the generation of important new concepts coincides with the obsolescence of existing concepts. We also find that the generation of important new concepts and highly cited work is more risky. Finally, supporting the validity of our metrics, we show that work that draws on a wider range of research fields is used more widely.

Published
2018