The Treasury Chest of Text Mining: Piling Available Resources for Powerful Biomedical Text Mining

BioChem Pub Date : 2021-07-27 DOI:10.3390/biochem1020007
N. Rosário-Ferreira, C. Marques-Pereira, M. Pires, D. Ramalhão, N. Pereira, Victor Guimarães, Vítor Santos Costa, I. Moreira
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引用次数: 7

Abstract

Text mining (TM) is a semi-automatized, multi-step process, able to turn unstructured into structured data. TM relevance has increased upon machine learning (ML) and deep learning (DL) algorithms’ application in its various steps. When applied to biomedical literature, text mining is named biomedical text mining and its specificity lies in both the type of analyzed documents and the language and concepts retrieved. The array of documents that can be used ranges from scientific literature to patents or clinical data, and the biomedical concepts often include, despite not being limited to genes, proteins, drugs, and diseases. This review aims to gather the leading tools for biomedical TM, summarily describing and systematizing them. We also surveyed several resources to compile the most valuable ones for each category.
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文本挖掘的宝库:为强大的生物医学文本挖掘堆积可用资源
文本挖掘(TM)是一种半自动化的多步骤过程,能够将非结构化数据转换为结构化数据。机器学习(ML)和深度学习(DL)算法在其各个步骤中的应用增加了TM的相关性。当应用于生物医学文献时,文本挖掘被称为生物医学文本挖掘,其特殊性在于分析文档的类型和检索的语言和概念。可以使用的文件阵列范围从科学文献到专利或临床数据,生物医学概念通常包括(尽管不限于)基因、蛋白质、药物和疾病。本文旨在收集生物医学TM的主要工具,对其进行概述和系统化。我们还调查了一些资源,为每个类别编译最有价值的资源。
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