Drug-side effect prediction has become an essential area of research in the
field of pharmacology. As the use of medications continues to rise, so does the
importance of understanding and mitigating the potential risks associated with
them. At present, researchers have turned to data-driven methods to predict
drug-side effects. Drug-side effect prediction is a link prediction problem,
and the related data can be described from various perspectives. To process
these kinds of data, a multi-view method, called Multiple Kronecker RLS
fusion-based link propagation (MKronRLSF-LP), is proposed. MKronRLSF-LP extends
the Kron-RLS by finding the consensus partitions and multiple graph Laplacian
constraints in the multi-view setting. Both of these multi-view settings
contribute to a higher quality result. Extensive experiments have been
conducted on drug-side effect datasets, and our empirical results provide
evidence that our approach is effective and robust.

药物副作用预测是药理学领域研究的一个关键领域。这篇论文引入了一个多视图方法，称为 MKronRLSF-LP，以预测药物副作用。研究通过对药物副作用数据进行广泛实验证明该方法是有效且稳健的。

基于多个 Kronecker RLS 融合的链路传播用于药物副作用预测

Multiple Kronecker RLS fusion-based link propagation for drug-side  effect prediction

Natural language processing (NLP) is an area of artificial intelligence that
applies information technologies to process the human language, understand it
to a certain degree, and use it in various applications. This area has rapidly
developed in the last few years and now employs modern variants of deep neural
networks to extract relevant patterns from large text corpora. The main
objective of this work is to survey the recent use of NLP in the field of
pharmacology. As our work shows, NLP is a highly relevant information
extraction and processing approach for pharmacology. It has been used
extensively, from intelligent searches through thousands of medical documents
to finding traces of adversarial drug interactions in social media. We split
our coverage into five categories to survey modern NLP methodology, commonly
addressed tasks, relevant textual data, knowledge bases, and useful programming
libraries. We split each of the five categories into appropriate subcategories,
describe their main properties and ideas, and summarize them in a tabular form.
The resulting survey presents a comprehensive overview of the area, useful to
practitioners and interested observers.

该研究论文调查了自然语言处理在药学领域中的应用，包括用于信息提取和处理的深度神经网络等。作者将其总结为五个类别并提出现代 NLP 方法、普遍任务、相关文本数据、知识库和有用的编程库等。总结为一份综合性概述，对实践者和感兴趣的观察者有用。