Evo learns biological complexity from the molecular to genome scale

IF 33.1 1区生物学 Q1 BIOTECHNOLOGY & APPLIED MICROBIOLOGY Nature biotechnology Pub Date : 2024-12-11 DOI:10.1038/s41587-024-02514-7

Iris Marchal

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超能力从分子到基因组级别学习生物复杂性

分子生物学的生成式人工智能模型通常局限于单个分子或DNA片段，并且在应用于长序列时，其构建方式使得它们的计算要求很高。捕捉更广泛的基因组相互作用的能力对于理解和设计复杂的生物过程至关重要。Nguyen等人在《科学》杂志上撰文介绍了Evo，这是一种基因组基础模型，可以在保持单核苷酸分辨率的情况下，在全基因组规模上解释和生成DNA序列。Evo建立在StripedHyena架构上，配备了70亿个参数，上下文长度高达131千碱基。经过270万个微生物基因组的训练，Evo在以前使用特定领域模型执行的各种任务上表现良好。例如，Evo了解了突变对蛋白质和非编码RNA功能的影响，模拟了调控元件的活性，并通过预测基因的必要性了解了小突变如何影响生物体的适应性。

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来源期刊

Nature biotechnology 工程技术-生物工程与应用微生物

CiteScore

63.00

自引率

1.70%

发文量

382

审稿时长

3 months

期刊介绍： Nature Biotechnology is a monthly journal that focuses on the science and business of biotechnology. It covers a wide range of topics including technology/methodology advancements in the biological, biomedical, agricultural, and environmental sciences. The journal also explores the commercial, political, ethical, legal, and societal aspects of this research. The journal serves researchers by providing peer-reviewed research papers in the field of biotechnology. It also serves the business community by delivering news about research developments. This approach ensures that both the scientific and business communities are well-informed and able to stay up-to-date on the latest advancements and opportunities in the field. Some key areas of interest in which the journal actively seeks research papers include molecular engineering of nucleic acids and proteins, molecular therapy, large-scale biology, computational biology, regenerative medicine, imaging technology, analytical biotechnology, applied immunology, food and agricultural biotechnology, and environmental biotechnology. In summary, Nature Biotechnology is a comprehensive journal that covers both the scientific and business aspects of biotechnology. It strives to provide researchers with valuable research papers and news while also delivering important scientific advancements to the business community.

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