Evolution of machine learning in biosciences: A bibliometric network analysis

Akkinepally Vanaja Venkata Rajesh Yella   

Open Access   

Published:  Jun 08, 2022

Abstract

Machine learning, a rapidly evolving field of data analysis, has now become an integral part of life science research. It has been widely utilized for exploring the information encoded by the genome and beyond the genome. In this study, we surveyed the trends of scientific actors and the conceptual structure of machine learning implementation in biomedical research through the published literature retrieved from the PubMed search engine. A longitudinal cohort bibliographic coupling was executed by employing the VOS viewer tool for 4-time periods, 1964–2010, 2011–2015, 2016–2018, and 2019–2020. Scientific actors of machine learning research include 42,629 unique authors, 27,364 organizations with a mean collaboration index of 3.9. Coword analysis revealed that the conceptual framework of machine learning applications in life sciences moved from simple pattern searching to omic science and medical imaging analytic approaches and from Bayes’ theorem to deep learning algorithms. It is observed that presently machine learning is extensively utilized in investigating emerging situations like coronavirus disease. To epitomize, researchers capitalized on advancements in machine learning tools and high-throughput technologies to delve into the intricate and evolving concepts of biology and medicine.


Keyword:     Machine learning Science mapping Bibliographic coupling VOS viewer tool PubMed.


Citation:

Vanaja A, Yella VR. Evolution of machine learning in biosciences: A bibliometric network analysis. J App Biol Biotech. 2022.

Copyright: Author(s). This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike license.

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Reference

1. Samuel AL. Some studies in machine learning using the game of checkers. IBM J Res Dev 1959;3:210-29. https://doi.org/10.1147/rd.33.0210

2. Guzella TS, Caminhas WM. Areview of machine learning approaches to spam filtering. Expert Syst Appl 2009;36:10206-22. https://doi.org/10.1016/j.eswa.2009.02.037

3. Srivani I, Prasad GS, Ratnam DV. A deep learning-based approach to forecast ionospheric delays for GPS signals. IEEE Geosci Remote Sens Lett 2019;16:1180-4. https://doi.org/10.1109/LGRS.2019.2895112

4. Bashir AK, Arul R, Basheer S, Raja G, Jayaraman R, Qureshi NM. An optimal multitier resource allocation of cloud RAN in 5G using machine learning. Trans Emerg Telecommun Technol 2019;30:e3627. https://doi.org/10.1002/ett.3627

5. Appathurai A, Sundarasekar R, Raja C, Alex EJ, Palagan CA. Nithya A. An efficient optimal neural network-based moving vehicle detection in traffic video surveillance system. Circuits Syst Signal Proc 2020;39:734-56. https://doi.org/10.1007/s00034-019-01224-9

6. Bojarski M, Del Testa D, Dworakowski D, Firner B, Flepp B Goyal P, et al. End to end learning for self-driving cars. arXiv preprint arXiv:1604.07316; 2016.

7. Rao GA, Kishore PV, Sastry AS, Anil Kumar D, Kiran Kumar E. Selfie continuous sign language recognition with neural network classifier. In: Proceedings of 2nd International Conference on MicroElectronics, Electromagnetics and Telecommunications. Berlin, Germany: Springer; 2018.

8. Liu SS, Tian YT. Facial expression recognition method based on gabor wavelet features and fractional power polynomial kernel PCA. In: International Symposium on Neural Networks. Berlin, Germany: Springer; 2010. https://doi.org/10.1007/978-3-642-13318-3_19

9. Nadh VL, Prasad GS. Stock market prediction based on machine learning approaches. In: Computational Intelligence and Big Data Analytics. Berlin, Germany: Springer; 2019. p. 75-9. https://doi.org/10.1007/978-981-13-0544-3_7

10. Lakshmi AV, Muzammil VG, Parvez M, Subhani SK, Ghali VS. Artificial neural networks based quantitative evaluation of subsurface anomalies in quadratic frequency modulated thermal wave imaging. Infrared Phys Technol 2019;97:108-15. https://doi.org/10.1016/j.infrared.2018.12.013

11. Pazzani M, Billsus D. Learning and revising user profiles: The identification of interesting web sites. Mach Learn 1997;27:313-31. https://doi.org/10.1023/A:1007369909943

12. Hood L, Heath JR, Phelps ME, Lin B. Systems biology and new technologies enable predictive and preventative medicine. Science 2004;306:640-3. https://doi.org/10.1126/science.1104635

13. Nielsen H, Brunak S, von Heijne G. Machine learning approaches for the prediction of signal peptides and other protein sorting signals. Protein Eng 1999;12:3-9. https://doi.org/10.1093/protein/12.1.3

14. Kourou K, Exarchos TP, Exarchos KP, Karamouzis MV, Fotiadis DI. Machine learning applications in cancer prognosis and prediction. Comput Struct Biotechnol J 2015;13:8-17. https://doi.org/10.1016/j.csbj.2014.11.005

15. Changala R, Rao DR. Development of predictive model for medical domains to predict chronic diseases (diabetes) using machine learning algorithms and classification techniques. ARPN J Eng Appl Sci 2019;14:1202-12.

16. Sajana T, Narasingarao M. An ensemble framework for classification of malaria disease. ARPN J Eng Appl Sci 2018;13:3299-307.

17. Sivakumar S, Nayak SR, Vidyanandini S, Jayaraman AK, Palai P. An empirical study of supervised learning methods for breast cancer diseases. Optik 2018;175:105-14. https://doi.org/10.1016/j.ijleo.2018.08.112

18. Libbrecht MW, Noble WS. Machine learning applications in genetics and genomics. Nat Rev Genetics 2015;16:321-32. https://doi.org/10.1038/nrg3920

19. Lemm S, Blankertz B, Dickhaus T, Müller KR. Introduction to machine learning for brain imaging. Neuroimage 2011;56:387-99. https://doi.org/10.1016/j.neuroimage.2010.11.004

20. Patil JS, Pradeepini G. Brain tumor levels detection in three dimensional MRI using machine learning and MapReduce. Indian J Public Health Res Dev 2019;10:1465-71. https://doi.org/10.5958/0976-5506.2019.01505.5

21. Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med 2019;380:1347-58. https://doi.org/10.1056/NEJMra1814259

22. Gutiérrez-Salcedo M, Ángeles Martínez M, Moral-Munoz JA, HerreraViedma E, Cobo MJ. Some bibliometric procedures for analyzing and evaluating research fields. Appl Intell 2018;48:1275-87. https://doi.org/10.1007/s10489-017-1105-y

23. Rincon-Patino J, Ramirez-Gonzalez G, Corrales JC. Exploring machine learning: A bibliometric general approach using SciMAT. F1000Research 2018;7:1210. https://doi.org/10.12688/f1000research.15620.1

24. Bhattacharya S. Some salient aspects of machine learning research: A bibliometric analysis. J Sci Res 2019;8:s85-92. https://doi.org/10.5530/jscires.8.2.26

25. Van Eck NJ, Waltman L. Visualizing bibliometric networks. In: Measuring Scholarly Impact. Berlin, Germany: Springer; 2014. p. 285-320. https://doi.org/10.1007/978-3-319-10377-8_13

26. Ho YS, Wang MH. A bibliometric analysis of artificial intelligence publications from 1991 to 2018. COLLNET J Scientometrics Inf Manag 2020;14:369-92. https://doi.org/10.1080/09737766.2021.1918032

27. dos Santos BS, Steinera MT, Fenericha MT, Lima RH. Data mining and machine learning techniques applied to public health problems: A bibliometric analysis from 2009 to 2018. Comput Ind Eng 2019;138:106120. https://doi.org/10.1016/j.cie.2019.106120

28. Kim J, Lee D, Park E. Machine learning for mental health in social media: Bibliometric study. J Med Internet Res 2021;23:e24870. https://doi.org/10.2196/24870

29. De Felice F, Polimeni A. Coronavirus disease (COVID-19): A machine learning bibliometric analysis. In Vivo 2020;34 3 suppl:1613-7. https://doi.org/10.21873/invivo.11951

30. Shukla N, Merigó JM, Lammers T, Miranda L. Half a century of computer methods and programs in biomedicine: A bibliometric analysis from 1970 to 2017. Comput Methods Programs Biomed 2020;183:105075. https://doi.org/10.1016/j.cmpb.2019.105075

31. Stallings J, Vance E, Yang J, Vannier MW, Liang J, Pang L, Dai L, et al. Determining scientific impact using a collaboration index. Proc Natl Acad Sci 2013;110:9680-5. https://doi.org/10.1073/pnas.1220184110

32. Topol EJ. High-performance medicine: The convergence of human and artificial intelligence. Nat Med 2019;25:44-56. https://doi.org/10.1038/s41591-018-0300-7

33. Guo Y, Hao Z, Zhao S, Gong J, Yang F. Artificial intelligence in health care: Bibliometric analysis. J Med Internet Res 2020;22:e18228. https://doi.org/10.2196/18228

34. Yadav M, Perumal M, Srinivas M. Analysis on novel coronavirus (COVID-19) using machine learning methods. Chaos Solitons Fractals 2020;139:110050. https://doi.org/10.1016/j.chaos.2020.110050

35. Liu C, Wang X, Liu C, Sun Q, Peng W. Differentiating novel coronavirus pneumonia from general pneumonia based on machine learning. Biomed Eng Online 2020;19:66. https://doi.org/10.1186/s12938-020-00809-9

36. Yao H, Zhang N, Zhang R, Duan M, Xie T, Pan J, et al. Severity detection for the coronavirus disease 2019 (COVID-19) patients using a machine learning model based on the blood and urine tests. Front Cell Dev Biol 2020;8:683. https://doi.org/10.3389/fcell.2020.00683

37. Ong E, Wong MU, Huffman A, He Y. COVID-19 coronavirus vaccine design using reverse vaccinology and machine learning. Front Immunol 2020;11:1581. https://doi.org/10.3389/fimmu.2020.01581

38. Liu Z, Huang S, Lu W, Su Z, Yin X, Liang H, et al. Modeling the trend of coronavirus disease 2019 and restoration of operational capability of metropolitan medical service in China: A machine learning and mathematical model-based analysis. Glob Health Res Policy 2020;5:1-11. https://doi.org/10.1186/s41256-020-00145-4

39. Heo L, Feig M. Modeling of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) proteins by machine learning and physics-based refinement. bioRxiv 2020;2020:8904. https://doi.org/10.1101/2020.03.25.008904

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