Natural language processing (NLP) is utilized in a wide range of fields,
where words in text are typically transformed into feature vectors called
embeddings. BioConceptVec is a specific example of embeddings tailored for
biology, trained on approximately 30 million PubMed abstracts using models such
as skip-gram. Generally, word embeddings are known to solve analogy tasks
through simple vector arithmetic. For instance, $\mathrm{\textit{king}} -
\mathrm{\textit{man}} + \mathrm{\textit{woman}}$ predicts
$\mathrm{\textit{queen}}$. In this study, we demonstrate that BioConceptVec
embeddings, along with our own embeddings trained on PubMed abstracts, contain
information about drug-gene relations and can predict target genes from a given
drug through analogy computations. We also show that categorizing drugs and
genes using biological pathways improves performance. Furthermore, we
illustrate that vectors derived from known relations in the past can predict
unknown future relations in datasets divided by year.

利用自然语言处理和 BioConceptVec 嵌入，预测药物和靶基因的关系，通过生物途径的分类改善性能，并通过历史关系的向量预测未知的未来关系。

利用词嵌入进行类比任务预测药物 - 基因关系

Predicting Drug-Gene Relations via Analogy Tasks with Word Embeddings

Capturing the semantics of related biological concepts, such as genes and
mutations, is of significant importance to many research tasks in computational
biology such as protein-protein interaction detection, gene-drug association
prediction, and biomedical literature-based discovery. Here, we propose to
leverage state-of-the-art text mining tools and machine learning models to
learn the semantics via vector representations (aka. embeddings) of over
400,000 biological concepts mentioned in the entire PubMed abstracts. Our
learned embeddings, namely BioConceptVec, can capture related concepts based on
their surrounding contextual information in the literature, which is beyond
exact term match or co-occurrence-based methods. BioConceptVec has been
thoroughly evaluated in multiple bioinformatics tasks consisting of over 25
million instances from nine different biological datasets. The evaluation
results demonstrate that BioConceptVec has better performance than existing
methods in all tasks. Finally, BioConceptVec is made freely available to the
research community and general public via
this https URL

本篇研究提出了 BioConceptVec，通过利用最先进的文本挖掘工具和机器学习模型学习 PubMed 摘要中介绍的超过 400,000 个生物概念的向量表示（即嵌入），来捕捉相关概念的语义。BioConceptVec 已经在包括 9 个不同生物数据集的 2500 万实例的多个生物信息学任务中得到了全面评估，在所有任务中其性能均优于现有方法。最后，BioConceptVec 通过网站免费向研究社区和公众提供。