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Analyzing topic evolution in bioinformatics: investigation of dynamics of the field with conference data in DBLP

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Abstract

In this paper we analyze topic evolution over time within bioinformatics to uncover the underlying dynamics of that field, focusing on the recent developments in the 2000s. We select 33 bioinformatics related conferences indexed in DBLP from 2000 to 2011. The major reason for choosing DBLP as the data source instead of PubMed is that DBLP retains most bioinformatics related conferences, and to study dynamics of the field, conference papers are more suitable than journal papers. We divide a period of a dozen years into four periods: period 1 (2000–2002), period 2 (2003–2005), period 3 (2006–2008) and period 4 (2009–2011). To conduct topic evolution analysis, we employ three major procedures, and for each procedure, we develop the following novel technique: the Markov Random Field-based topic clustering, automatic cluster labeling, and topic similarity based on Within-Period Cluster Similarity and Between-Period Cluster Similarity. The experimental results show that there are distinct topic transition patterns between different time periods. From period 1 to period 3, new topics seem to have emerged and expanded, whereas from period 3 to period 4, topics are merged and display more rigorous interaction with each other. This trend is confirmed by the collaboration pattern over time.

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Acknowledgments

This work was supported by National Research Foundation of Korea Grant funded by the Korean Government (NRF-2012-2012S1A3A2033291) and by the National Research Foundation of Korea (NRF) grant funded by the Korea government (No. 2012033242).

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Authors and Affiliations

  1. Department of Library and Information Science, Yonsei University, 50 Yonsei-ro Seodaemun-gu, Seoul, Korea

    Min Song, Go Eun Heo & Su Yeon Kim

Authors
  1. Min Song
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Corresponding author

Correspondence to Min Song.

Appendices

Appendix 1: The list of conference full names

Conference name

Conference full name

APBC

Asia–Pacific Bioinformatics Conference

AVBPA

Audio- and Video-Based Biometric Person Authentication

BIBE

Bioinformatics and Bioengineering

BIBM

Bioinformatics and Biomedicine

BIDM

Biological Data Management

B-interface

Bio-inspired Human–Machine Interfaces and Healthcare Applications

BIOCOMP

Bioinformatics & Computational Biology

BIODEVICES

Biomedical Electronics and Devices

BIOINFORMATICS

International Conference on Bioinformatics

BIOSIG

Biometrics and Electronic Signatures

BIOSIGNALS

Bio-inspired Systems and Signal Processing

BIOSTEC

Biomedical Engineering Systems and Technologies

BMEI

BioMedical Engineering and Informatics

BSBT

Bio-Science and Bio-Technology

CIBCB

Computational Intelligence in Bioinformatics and Computational Biology

CMSB

Computational Methods in Systems Biology

CSB

Computational Systems Bioinformatics

DNA Computing

DNA Computing

ECCB

European Conference on Computational Biology

EuroGP

European Conference on Genetic Programming

EvoBIO

Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics

FOGA

Foundations of Genetic Algorithms

GCB

German Conference on Bioinformatics

GECCO

Genetic and Evolutionary Computation Conference

ISBI

International Symposium on Biomedical Imaging

ISBRA

International Symposium on Bioinformatics Research and Applications

ISMB

Intelligent Systems in Molecular Biology

PRIB

Pattern Recognition in Bioinformatics

PSB

Pacific Symposium on Biocomputing

RECOMB

Research in Computational Molecular Biology

WABI

Workshop on Algorithms in Bioinformatics

WBIR

Workshop on Biomedical Image Registration

WOB

Brazilian Workshop on Bioinformatics

Appendix 2: Cluster similarity between clusters by BPCS for the “same” relation

Period

Similarity

1

2

Value

Cluster 11

Cluster 8

0.797

Cluster 17

Cluster 0

0.778

Cluster 7

Cluster 10

0.770

Cluster 17

Cluster 8

0.764

Cluster 3

Cluster 6

0.763

Cluster 17

Cluster 14

0.756

Cluster 14

Cluster 14

0.750

Cluster 6

Cluster 8

0.748

Cluster 4

Cluster 7

0.747

Cluster 9

Cluster 5

0.747

Cluster 17

Cluster 6

0.745

Cluster 7

Cluster 8

0.736

Cluster 7

Cluster 14

0.735

2

3

Value

Cluster 8

Cluster 10

0.848

Cluster 0

Cluster 2

0.836

Cluster 3

Cluster 16

0.820

Cluster 4

Cluster 10

0.815

Cluster 14

Cluster 8

0.813

Cluster 12

Cluster 17

0.801

Cluster 1

Cluster 0

0.800

Cluster 12

Cluster 1

0.799

Cluster 12

Cluster 8

0.794

Cluster 3

Cluster 9

0.793

Cluster 7

Cluster 9

0.789

Cluster 15

Cluster 9

0.785

Cluster 1

Cluster 19

0.784

Cluster 14

Cluster 14

0.784

3

4

Value

Cluster 8

Cluster 3

0.846

Cluster 5

Cluster 17

0.833

Cluster 2

Cluster 0

0.830

Cluster 4

Cluster 17

0.829

Cluster 12

Cluster 8

0.824

Cluster 19

Cluster 14

0.823

Cluster 10

Cluster 14

0.823

Cluster 15

Cluster 3

0.822

Cluster 10

Cluster 3

0.810

Cluster 9

Cluster 11

0.805

Cluster 0

Cluster 18

0.800

Cluster 16

Cluster 3

0.797

Cluster 8

Cluster 17

0.794

Cluster 6

Cluster 5

0.793

Cluster 18

Cluster 5

0.793

Cluster 5

Cluster 14

0.793

Cluster 10

Cluster 17

0.792

Cluster 2

Cluster 17

0.790

Cluster 11

Cluster 3

0.789

Appendix 3: Topic transition with top-level MeSH terms from period 1 to period 4

Appendix 4: Cluster similarity within a period by WPCS for the “same” relation

Period

Similarity

1

Value

Cluster 7

Cluster 11

0.729

Cluster 7

Cluster 12

0.706

Cluster 9

Cluster 16

0.704

Cluster 12

Cluster 13

0.699

Cluster 7

Cluster 9

0.689

Cluster 1

Cluster 9

0.677

2

Value

Cluster 5

Cluster 14

0.814

Cluster 7

Cluster 13

0.806

Cluster 0

Cluster 3

0.801

Cluster 7

Cluster 15

0.800

Cluster 6

Cluster 8

0.783

Cluster 7

Cluster 14

0.772

Cluster 12

Cluster 16

0.767

Cluster 1

Cluster 13

0.766

Cluster 4

Cluster 18

0.760

3

Value

Cluster 4

Cluster 10

0.813

Cluster 2

Cluster 10

0.791

Cluster 2

Cluster 5

0.788

Cluster 0

Cluster 2

0.778

Cluster 0

Cluster 4

0.772

Cluster 5

Cluster 10

0.769

4

Value

Cluster 14

Cluster 17

0.860

Cluster 3

Cluster 14

0.817

Cluster 3

Cluster 17

0.811

Cluster 2

Cluster 17

0.799

Cluster 5

Cluster 14

0.796

Cluster 3

Cluster 13

0.789

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Song, M., Heo, G.E. & Kim, S.Y. Analyzing topic evolution in bioinformatics: investigation of dynamics of the field with conference data in DBLP. Scientometrics 101, 397–428 (2014). https://doi.org/10.1007/s11192-014-1246-2

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