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Discovering similarities for the treatments of liver specific parasites

机译:发现治疗肝特定寄生虫的相似性

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Medline articles are rich resources for discovering hidden knowledge for the treatments of liver specific parasites. Knowledge acquisition from these articles requires complex processes depending on biomedical text mining techniques. In this study, name entity recognition and hierarchical clustering techniques were used for advanced drug analyses. Drugs were extracted from the articles belonging to specific time periods and hierarchical clustering was applied on parasite and drug datasets. Hierarchical clustering results revealed that some parasites have similar in terms of treatment and the others are different. Our results also showed that, there have not been major changes in the treatment of liver specific parasites for the past four decades and there are problems associated with the development of new drugs. Both pharmaceutical initiatives and healthcare providers should investigate major drawbacks and develop some strategies to overcome these problems.
机译:Medline文章是丰富的资源,用于发现肝脏特异性寄生虫治疗的隐藏知识。根据生物医学文本挖掘技术,这些文章的知识获取需要复杂的流程。在本研究中,名称实体识别和分层聚类技术用于高级药物分析。从属于特定时间段的物品中提取药物,并在寄生虫和药物数据集上施加分层聚类。分层聚类结果表明,一些寄生虫在治疗方面具有相似,其他寄生虫具有类似的寄生虫。我们的研究结果还表明,过去四十年肝脏特异性寄生虫的治疗并未发生重大变化,并且存在与新药的发展有关的问题。药品举措和医疗保健提供者都应调查主要缺点并制定一些克服这些问题的策略。

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