Zenodo

Drug toxicity is a major concern for both regulatory agencies and the pharmaceutical industry. A new system for identification of drug side effects from the literature was presented that combines three approaches: machine learning, rule- and knowledge-based approaches. This system has been developed to address the Task 3.B of Biocreative V challenge (BC5) dealing with Chemical-induced Disease (CID) relations.药物毒性始终是监管机构与制药行业共同关注的核心议题。本研究提出了一种可从学术文献中识别药物不良反应的新型系统，该系统融合了三类技术路径：机器学习方法、基于规则的方法以及基于知识的方法。该系统专为解决生物创意挑战赛第五届（Biocreative V Challenge, BC5）中的任务3.B而开发，该任务聚焦于化学物质诱导疾病（Chemical-induced Disease, CID）的关联关系识别。

搜集汇总

背景与挑战

背景概述

该数据集是一个通过众包方式构建的化学诱导疾病关系语料库，专为BioCreative V挑战而创建。它包含句子级别的标注数据，用于支持相关研究任务。