Zheng Si, Dharssi Shazia, Wu Meng, Li Jiao, Lu Zhiyong
Institute of Medical Information and Library, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD, USA.
Methods Mol Biol. 2019;1939:231-252. doi: 10.1007/978-1-4939-9089-4_13.
Recent advances in technology have led to the exponential growth of scientific literature in biomedical sciences. This rapid increase in information has surpassed the threshold for manual curation efforts, necessitating the use of text mining approaches in the field of life sciences. One such application of text mining is in fostering in silico drug discovery such as drug target screening, pharmacogenomics, adverse drug event detection, etc. This chapter serves as an introduction to the applications of various text mining approaches in drug discovery. It is divided into two parts with the first half as an overview of text mining in the biosciences. The second half of the chapter reviews strategies and methods for four unique applications of text mining in drug discovery.
技术的最新进展导致了生物医学科学领域科学文献的指数级增长。信息的这种快速增长已经超过了人工整理工作的阈值，因此生命科学领域需要使用文本挖掘方法。文本挖掘的一个此类应用是促进计算机辅助药物发现，例如药物靶点筛选、药物基因组学、药物不良事件检测等。本章介绍了各种文本挖掘方法在药物发现中的应用。它分为两部分，前半部分是生物科学中文本挖掘的概述。本章后半部分回顾了文本挖掘在药物发现中的四个独特应用的策略和方法。
