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Bulletin of Chinese Academy of Sciences (Chinese Version)

Keywords

scientific research, life science, artificial intelligence, big data, scientific paradigm

Document Type

Vigorously Promote Scientific Research Paradigm Transform

Abstract

The rapid development of biotechnology and information technology has brought life sciences into a new era of data explosion. The traditional life science research paradigm struggles to reveal the fundamental rules of complex biological systems from rapidly growing biological big data. As artificial intelligence continues to achieve disruptive breakthroughs in life science, a new paradigm driven by AI is emerging. This study delves into typical examples of life science research driven by AI, proposes the concept and key elements of the new life science research paradigm, elaborates on the cutting-edge of life science research under this new paradigm, and discusses the challenges in China.

First page

50

Last Page

58

Language

Chinese

Publisher

Bulletin of Chinese Academy of Sciences

References

1 Wang H, Fu T, Du Y, et al. Scientific discovery in the age of artificial intelligence. Nature, 2023, 620: 47-60.

2 Erbe R, Gore J, Gemmill K, et al. The use of machine learning to discover regulatory networks controlling biological systems. Molecular Cell, 2022, 82(2): 260-273.

3 Jumper J, Evans R, Pritzel A, et al. Highly accurate protein structure prediction with AlphaFold. Nature, 2021, 596: 583-589.

4 Borkakoti N, Thornton J M. AlphaFold2 protein structure prediction: Implications for drug discovery. Current Opinion in Structural Biology, 2023, 78: 102526.

5 Huang J Y, Lin Q P, Fei H Y, et al. Discovery of deaminase functions by structure-based protein clustering. Cell, 2023, 186(15): 3182-3195.e14.

6 Madani A, Krause B, Greene E R, et al. Large language models generate functional protein sequences across diverse families. Nature Biotechnology, 2023, 41(8): 1099-1106.

7 Yang X D, Liu G L, Feng G H, et al. GeneCompass: Deciphering universal gene regulatory mechanisms with knowledge-informed cross-species foundation model. (2023-09-28). https://www.biorxiv.org/content/10.1101/2023.09.26.559542v1.

8 Cui H T, Wang C, Maan H, et al. scGPT: Towards building a foundation model for single-cell multi-omics using generative AI. (2023-05-01). https://www.biorxiv.org/content/10.1101/2023.04.30.538439v1.

9 Theodoris C V, Xiao L, Chopra A, et al. Transfer learning enables predictions in network biology. Nature, 2023, 618: 616-624.

10 Hao M S, Gong J, Zeng X, et al. Large scale foundation model on single-cell transcriptomics. (2023-05-31). https://www.biorxiv.org/content/10.1101/2023.05.29.542705v1.

11 Singhal K, Azizi S, Tu T, et al. Large language models encode clinical knowledge. Nature, 2023, 620: 172-180.

12 Moor M, Banerjee O, Abad Z S H, et al. Foundation models for generalist medical artificial intelligence. Nature, 2023, 616: 259-265.

13 Li C Y, Wong C, Zhang S, et al. LLaVA-med: Training a large language-and-vision assistant for biomedicine in one day. (2023-06-02). https://arxiv.org/abs/2306.00890.

14 Alber M, Buganza Tepole A, Cannon W R, et al. Integrating machine learning and multiscale modeling-perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences. NPJ Digital Medicine, 2019, 2: 115.

15 Jansen R, Yu H Y, Greenbaum D, et al. A Bayesian networks approach for predicting protein-protein interactions from genomic data. Science, 2003, 302: 449-453.

16 Wu Y F, Gao M, Zeng M, et al. BridgeDPI: A novel Graph Neural Network for predicting drug–protein interactions. Bioinformatics, 2022, 38(9): 2571-2578.

17 Gan Y L, Hu X, Zou G B, et al. Inferring gene regulatory networks from single-cell transcriptomic data using bidirectional RNN. Frontiers in Oncology, 2022, 12: 899825.

18 Lutz I D, Wang S Z, Norn C, et al. Top-down design of protein architectures with reinforcement learning. Science, 2023, 380: 266-273.

19 Watson J L, Juergens D, Bennett N R, et al. De novo design of protein structure and function with RFdiffusion. Nature, 2023, 620: 1089-1100.

20 Kermany D S, Goldbaum, Cai W.M. & Leveraging big data and AI in medical diagnosis. [2023-12-30]. https://www.nature.com/articles/d42473-022-00035-y.

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