Large Language Models (LLMs), such as ChatGPT, have taken the world by storm
and have passed certain forms of the Turing test. However, LLMs are not limited
to human language and analyze sequential data, such as DNA, protein, and gene
expression. The resulting foundation models can be repurposed to identify the
complex patterns within the data, resulting in powerful, multi-purpose
prediction tools able to explain cellular systems. This review outlines the
different types of LLMs and showcases their recent uses in biology. Since LLMs
have not yet been embraced by the plant community, we also cover how these
models can be deployed for the plant kingdom.

大语言模型如 ChatGPT 不仅限于人类语言，还可以分析 DNA、蛋白质和基因表达等串联数据，从而生成可应用于复杂模式识别和生物学解释的基础模型，本文评述了不同类型的大语言模型，并展示了它们在生物学领域中的最新应用，同时探讨了这些模型如何在植物界中进行部署。

植物生物学中的大型语言模型

Large Language Models in Plant Biology

We introduce a novel Bayesian hybrid matrix factorisation model (HMF) for
data integration, based on combining multiple matrix factorisation methods,
that can be used for in- and out-of-matrix prediction of missing values. The
model is very general and can be used to integrate many datasets across
different entity types, including repeated experiments, similarity matrices,
and very sparse datasets. We apply our method on two biological applications,
and extensively compare it to state-of-the-art machine learning and matrix
factorisation models. For in-matrix predictions on drug sensitivity datasets we
obtain consistently better performances than existing methods. This is
especially the case when we increase the sparsity of the datasets. Furthermore,
we perform out-of-matrix predictions on methylation and gene expression
datasets, and obtain the best results on two of the three datasets, especially
when the predictivity of datasets is high.

引入了一种新的贝叶斯混合矩阵分解模型（HMF）用于数据集成，能够结合多种矩阵分解方法，可用于预测缺失值的内部和外部矩阵，同时可集成许多不同实体类型的数据集，包括重复实验、相似度矩阵和非常稀疏的数据集。我们将方法应用于两个生物应用，并与最先进的机器学习和矩阵分解模型进行广泛比较。对于药物敏感性数据集的内部矩阵预测，我们获得了比现有方法更优异的性能，特别是当我们增加数据集的稀疏性时。此外，我们还对甲基化和基因表达数据集进行了外部矩阵预测，在其中三个数据集中有两个获得了最佳结果，特别是当数据集的可预测性高时。