On knowing a gene: A distributional hypothesis of gene function

IF 9 1区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY Cell Systems Pub Date : 2024-05-28 DOI:10.1016/j.cels.2024.04.008
Jason J. Kwon, Joshua Pan, Guadalupe Gonzalez, William C. Hahn, Marinka Zitnik
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Abstract

As words can have multiple meanings that depend on sentence context, genes can have various functions that depend on the surrounding biological system. This pleiotropic nature of gene function is limited by ontologies, which annotate gene functions without considering biological contexts. We contend that the gene function problem in genetics may be informed by recent technological leaps in natural language processing, in which representations of word semantics can be automatically learned from diverse language contexts. In contrast to efforts to model semantics as “is-a” relationships in the 1990s, modern distributional semantics represents words as vectors in a learned semantic space and fuels current advances in transformer-based models such as large language models and generative pre-trained transformers. A similar shift in thinking of gene functions as distributions over cellular contexts may enable a similar breakthrough in data-driven learning from large biological datasets to inform gene function.

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认识基因基因功能分布假说
单词可以有多种含义,取决于句子的上下文,基因也可以有多种功能,取决于周围的生物系统。基因功能的这种多义性受到本体论的限制,本体论只注释基因功能,而不考虑生物背景。我们认为,遗传学中的基因功能问题可以借鉴自然语言处理领域最近的技术飞跃,在自然语言处理领域,单词语义的表征可以从不同的语言上下文中自动学习。与 20 世纪 90 年代将语义建模为 "is-a "关系的努力不同,现代分布语义学将单词表示为学习语义空间中的向量,并推动了目前基于转换器的模型(如大型语言模型和生成预训练转换器)的进步。将基因功能视为细胞上下文分布的类似思维转变,可能会在从大型生物数据集进行数据驱动学习以了解基因功能方面带来类似的突破。
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来源期刊
Cell Systems
Cell Systems Medicine-Pathology and Forensic Medicine
CiteScore
16.50
自引率
1.10%
发文量
84
审稿时长
42 days
期刊介绍: In 2015, Cell Systems was founded as a platform within Cell Press to showcase innovative research in systems biology. Our primary goal is to investigate complex biological phenomena that cannot be simply explained by basic mathematical principles. While the physical sciences have long successfully tackled such challenges, we have discovered that our most impactful publications often employ quantitative, inference-based methodologies borrowed from the fields of physics, engineering, mathematics, and computer science. We are committed to providing a home for elegant research that addresses fundamental questions in systems biology.
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