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Journal of Data and Information Science  2019, Vol. 4 Issue (4): 13-25    DOI: 10.2478/jdis-2019-0018
Research Paper     
A Metric Approach to Hot Topics in Biomedicine via Keyword Co-occurrence
Jane H. Qin1,2,,Jean J. Wang1,2,Fred Y. Ye1()
1Jiangsu Key Laboratory of Data Engineering and Knowledge Service, School of Information Management, Nanjing University, Nanjing 210023, China
2International Joint Informatics Laboratory (IJIL), Nanjing University - University of Illinois, Nanjing - Champaign, China - USA
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Abstract  

Purpose: To reveal the research hotpots and relationship among three research hot topics in biomedicine, namely CRISPR, iPS (induced Pluripotent Stem) cell and Synthetic biology.

Design/methodology/approach: We set up their keyword co-occurrence networks with using three indicators and information visualization for metric analysis.

Findings: The results reveal the main research hotspots in the three topics are different, but the overlapping keywords in the three topics indicate that they are mutually integrated and interacted each other.

Research limitations: All analyses use keywords, without any other forms.

Practical implications: We try to find the information distribution and structure of these three hot topics for revealing their research status and interactions, and for promoting biomedical developments.

Originality/value: We chose the core keywords in three research hot topics in biomedicine by using h-index.



Key wordsco-occurrence      Network analysis      Information visualization      Biomedicine      Hot topics      CRISPR-Cas      iPS cell      Synthetic biology     
Received: 24 September 2019      Published: 19 December 2019
Corresponding Authors: Jane H. Qin     E-mail: yye@nju.edu.cn
Cite this article:

Jane H. Qin, Jean J. Wang, Fred Y. Ye. A Metric Approach to Hot Topics in Biomedicine via Keyword Co-occurrence. Journal of Data and Information Science, 2019, 4(4): 13-25.

URL:

http://manu47.magtech.com.cn/Jwk3_jdis/10.2478/jdis-2019-0018     OR     http://manu47.magtech.com.cn/Jwk3_jdis/Y2019/V4/I4/13

Rank Keyword Degree Centrality Keyword Betweenness Centrality
1 CRISPR 188 CRISPR 1,378.00
2 GENOME EDITING 156 GENOME EDITING 664.00
3 CRISPR/CAS 132 CRISPR/CAS 441.37
4 GENOME ENGINEERING 94 GENOME ENGINEERING 157.52
5 HOMOLOGOUS RECOMBINATION 84 GENES 135.44
6 GENES 76 HOMOLOGOUS RECOMBINATION 111.44
7 ZEBRAFISH 70 ZEBRAFISH 110.97
8 GENE TARGET 66 GENE REGULATION 60.97
9 TALEN 64 CRISPRI 53.39
10 SYNTHETIC BIOLOGY 58 APOPTOSIS 49.33
11 GENE REGULATION 56 DNA REPAIR 48.34
12 GENE THERAPY 56 SYNTHETIC BIOLOGY 47.97
13 DNA REPAIR 54 GENE TARGET 47.13
14 GENE KNOCKOUT 54 IPSC 42.64
15 IPSC 50 GENE KNOCKOUT 41.15
16 ZFN 50 TALEN 41.09
17 SGRNA 46 GENE THERAPY 39.40
18 CANCER 42 EVOLUTION 37.89
19 CRRNA 42 SGRNA 34.15
20 EVOLUTION 42 CANCER 34.14
Table 1 Co-occurrence network centrality with the highest h-index keywords in the field of CRISPR/Cas9.
Figure 1. Co-occurrence network with the highest h-index keywords in the field of CRISPR/Cas9.
Figure 2. Co-occurrence network with the highest h-index keywords in the field of iPS cell.
Rank Keyword Degree Centrality Keyword Betweenness Centrality
1 STEM CELL 184 STEM CELL 359.39
2 EMBRYONIC STEM CELL 170 EMBRYONIC STEM CELL 267.07
3 REPROGRAMMING 166 HUMAN IPSC 254.20
4 HUMAN IPSC 162 REPROGRAMMING 246.40
5 PLURIPOTENT STEM CELL 160 DIFFERENTIATION 225.41
6 DIFFERENTIATION 158 PLURIPOTENT STEM CELL 221.89
7 REGENERATIVE MEDICINE 118 HUMAN EMBRYONIC STEM CELL 110.40
8 HUMAN EMBRYONIC STEM CELL 116 REGENERATIVE MEDICINE 100.92
9 NEURAL STEM CELL 116 MESENCHYMAL STEM CELL 94.19
10 MESENCHYMAL STEM CELL 114 NEURAL STEM CELL 83.66
11 CELL THERAPY 102 TRANSPLANTATION 70.11
12 PLURIPOTENCY 102 PLURIPOTENCY 67.90
13 TRANSPLANTATION 102 CELL THERAPY 60.74
14 TISSUE ENGINEERING 98 CARDIOMYOCYTES 58.25
15 CARDIOMYOCYTES 90 NEURON 56.58
16 NEURON 90 TISSUE ENGINEERING 54.92
17 DISEASE MODELING 82 HIPSC 43.86
18 HIPSC 82 DRUG SCREENING 42.39
19 PARKINSON’S DISEASE 82 GENE EXPRESSION 40.25
20 GENE EXPRESSION 80 DISEASE MODELING 37.50
Table 2 Co-occurrence network centrality with the highest h-index keywords in the field of iPS cell.
Figure 3. Co-occurrence network with the highest h-index keywords in the field of synthetic biology.
Rank Keyword Degree
Centrality
Keyword Betweennes Centrality
1 METABOLIC ENGINEERING 100 METABOLIC ENGINEERING 378.42
2 YEAST 90 GENE EXPRESSION 312.93
3 GENE CIRCUIT 82 YEAST 286.57
4 ES CELLHERICHIA COLI 80 GENE CIRCUIT 281.48
5 GENE EXPRESSION 80 ES CELLHERICHIA COLI 249.28
6 SACCHAROMYCES CEREVISIAE 80 SACCHAROMYCES CEREVISIAE 229.17
7 PROTEIN ENGINEERING 66 SYNTHETIC GENE 180.28
8 DIRECTED EVOLUTION 62 PROTEIN ENGINEERING 171.01
9 GENE REGULATION 60 CELL CYCLE 169.32
10 SYNTHETIC GENE 60 GENE THERAPY 158.88
11 SYSTEMS BIOLOGY 60 TRANSCRIPTION 146.23
12 TRANSCRIPTION 60 DIRECTED EVOLUTION 143.33
13 GENE ENGINEERING 56 SYSTEMS BIOLOGY 130.95
14 BIOTECHNOLOGY 50 GENE REGULATION 108.56
15 GENE THERAPY 50 GENE ENGINEERING 90.24
16 CRISPR/CAS9 48 ESSENTIAL GENE 87.67
17 CYANOBACTERIA 46 CELL-FREE PROTEIN SYNjournal 76.44
18 ESSENTIAL GENE 46 EVOLUTION 71.90
19 EVOLUTION 44 TRANSCRIPTION FACTOR 70.90
20 E. COLI 42 BIOTECHNOLOGY 66.57
Table 3 Co-occurrence network centrality with the highest h-index keywords in the field of synthetic biology.
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