Application of artificial intelligence in life science: Historical review and future perspectives

The fast advancement of artificial intelligence (AI) technologies in recent years, especially the deep learning algorithm and transformer neural network, has brought great impacts on scientific researches. As a displine focusing on curation and analysis of life science data, bioinformatics has experienced remarkable changes under the impetus of AI technologies, not only in nowadays, but also throughout its history. In this review, we firstly summarize the historical events of computer-assisted life science data analysis, then assess the features, contributions and changes of AI methods in life science research by using bibliometric analysis, and discuss the future challenges for AI methods from the life science research aspects. There is no doubt that AI technologies will continuously accelerate and revolutionize life science research in the future, in the meanwhile, the development of new AI methods more suitable for life science data is also in great needs.

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Publication Details

Journal
Fundamental Research
Published
2024-11-08
DOI
https://doi.org/10.1016/j.fmre.2024.10.014
Citations
5
Primary Topic
Machine Learning in Bioinformatics
Type
article
Field-Weighted Citation Impact
1.17

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article

Application of artificial intelligence in life science: Historical review and future perspectives

Xiu‐Jie Wang, Meng Wang, Lei Shi
5 citations
Fundamental Research
Machine Learning in Bioinformatics
1.17
article

Application of artificial intelligence in life science: Historical review and future perspectives

Xiu‐Jie Wang, Meng Wang, Lei Shi
article en
5 citations

Abstract

The fast advancement of artificial intelligence (AI) technologies in recent years, especially the deep learning algorithm and transformer neural network, has brought great impacts on scientific researches. As a displine focusing on curation and analysis of life science data, bioinformatics has experienced remarkable changes under the impetus of AI technologies, not only in nowadays, but also throughout its history. In this review, we firstly summarize the historical events of computer-assisted life science data analysis, then assess the features, contributions and changes of AI methods in life science research by using bibliometric analysis, and discuss the future challenges for AI methods from the life science research aspects. There is no doubt that AI technologies will continuously accelerate and revolutionize life science research in the future, in the meanwhile, the development of new AI methods more suitable for life science data is also in great needs.

Fundamental ResearchVol. 6(1)
Chinese Academy of Sciences (CN), Institute of Genetics and Developmental Biology (CN), University of Chinese Academy of Sciences (CN)
National Natural Science Foundation of China
Zero hunger
Openalex Percentile: Top 21%
Machine Learning in Bioinformatics
1.17
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