Machine learning for accelerating discovery from single-molecule data

IF 32.1 1区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Nature Methods Pub Date : 2025-10-02 DOI:10.1038/s41592-025-02840-x

引用次数: 0

Abstract

Manual analysis of single-molecule time traces is slow and subjective. Now, a transformer-based foundation model — META-SiM —automates key analysis tasks across diverse datasets and enables rapid, systematic discovery of subtle single-molecule behaviors. Application of this approach reveals a previously undetected pre-mRNA splicing intermediate, highlighting its potential to streamline biological discovery.

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加速单分子数据发现的机器学习。

手工分析单分子时间轨迹是缓慢和主观的。现在，基于变压器的基础模型META-SiM可以自动完成跨不同数据集的关键分析任务，并能够快速、系统地发现细微的单分子行为。这种方法的应用揭示了一种以前未被发现的pre-mRNA剪接中间体，突出了其简化生物学发现的潜力。

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

Nature Methods 生物-生化研究方法

CiteScore

58.70

自引率

1.70%

发文量

326

审稿时长

1 months

期刊介绍： Nature Methods is a monthly journal that focuses on publishing innovative methods and substantial enhancements to fundamental life sciences research techniques. Geared towards a diverse, interdisciplinary readership of researchers in academia and industry engaged in laboratory work, the journal offers new tools for research and emphasizes the immediate practical significance of the featured work. It publishes primary research papers and reviews recent technical and methodological advancements, with a particular interest in primary methods papers relevant to the biological and biomedical sciences. This includes methods rooted in chemistry with practical applications for studying biological problems.