Artificial intelligence: the human response to approach the complexity of big data in biology.

IF 11.8 2区生物学 Q1 MULTIDISCIPLINARY SCIENCES

GigaScience Pub Date : 2025-01-06 DOI:10.1093/gigascience/giaf057

Giovanni Melandri, Georges R-Radohery, Chloé Beaumont, Sara M de Cripan, Coralie Muller, Luca Piras, Maria Alcina Pereira, Andreia Ferreira Salvador, Xavier Domingo-Almenara, Marie Bolger, Sophie Colombié, Sylvain Prigent, Biotza Gutierrez Arechederra, Nuria Canela Canela, Pierre Pétriacq

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引用次数: 0

Abstract

Since the late 2010s, artificial intelligence (AI), encompassing machine learning and propelled by deep learning, has transformed life science research. It has become a crucial tool for advancing the computational analysis of biological processes, the discovery of natural products, and the study of ecosystem dynamics. This review explores how the rapid increase in high-throughput omics data acquisition has driven the need for AI-based analysis in life sciences, with a particular focus on plant sciences, animal sciences, and microbiology. We highlight the role of omics-based predictive analytics in systems biology and innovative AI-based analytical approaches for gaining deeper insights into complex biological systems. Finally, we discuss the importance of FAIR (findable, accessible, interoperable, reusable) principles for omics data, as well as the future challenges and opportunities presented by the increasing use of AI in life sciences.

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人工智能：人类对接近生物学大数据复杂性的反应。

自2010年代末以来，包括机器学习和深度学习推动的人工智能（AI）已经改变了生命科学研究。它已成为推进生物过程的计算分析、天然产物的发现和生态系统动力学研究的重要工具。这篇综述探讨了高通量组学数据采集的快速增长如何推动了生命科学中对基于人工智能的分析的需求，特别是在植物科学、动物科学和微生物学方面。我们强调了基于组学的预测分析在系统生物学中的作用，以及创新的基于人工智能的分析方法，以深入了解复杂的生物系统。最后，我们讨论了组学数据的FAIR（可查找、可访问、可互操作、可重用）原则的重要性，以及人工智能在生命科学中越来越多地使用所带来的未来挑战和机遇。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

GigaScience MULTIDISCIPLINARY SCIENCES-

CiteScore

15.50

自引率

1.10%

发文量

119

审稿时长

1 weeks

期刊介绍： GigaScience seeks to transform data dissemination and utilization in the life and biomedical sciences. As an online open-access open-data journal, it specializes in publishing "big-data" studies encompassing various fields. Its scope includes not only "omic" type data and the fields of high-throughput biology currently serviced by large public repositories, but also the growing range of more difficult-to-access data, such as imaging, neuroscience, ecology, cohort data, systems biology and other new types of large-scale shareable data.