As a key technology of rapid and low-cost drug development, drug repositioning is getting popular. In this study, a text mining approach to the discovery of unknown drug-disease relation was tested. Using a word embed...As a key technology of rapid and low-cost drug development, drug repositioning is getting popular. In this study, a text mining approach to the discovery of unknown drug-disease relation was tested. Using a word embedding algorithm, senses of over 1.7 million words were well represented in sufficiently short feature vectors. Through various analysis including clustering and classification, feasibility of our approach was tested. Finally, our trained classification model achieved 87.6% accuracy in the prediction of drug-disease relation in cancer treatment and succeeded in discovering novel drug-disease relations that were actually reported in recent studies.展开更多
保护定值的正确性对充分发挥继电保护系统的作用至关重要,但目前定值比对仍采用人工方式,工作量大、时间长且结果正确性无法保证。对此,梳理了定值名称的命名特点,提出了一种基于神经网络的继电保护定值名称智能比对方法。首先进行文本...保护定值的正确性对充分发挥继电保护系统的作用至关重要,但目前定值比对仍采用人工方式,工作量大、时间长且结果正确性无法保证。对此,梳理了定值名称的命名特点,提出了一种基于神经网络的继电保护定值名称智能比对方法。首先进行文本预处理,然后将预处理后的定值文本向量化,最后使用双向长短时记忆(bi-directional long short-term memory,Bi-LSTM)神经网络计算定值名称语义特征向量相似度。算例表明,基于神经网络的定值名称智能比对方法能有效完成定值单和运行定值名称的匹配,且神经网络比模糊匹配处理定值名称匹配问题准确率更高,速度更快。展开更多
文摘As a key technology of rapid and low-cost drug development, drug repositioning is getting popular. In this study, a text mining approach to the discovery of unknown drug-disease relation was tested. Using a word embedding algorithm, senses of over 1.7 million words were well represented in sufficiently short feature vectors. Through various analysis including clustering and classification, feasibility of our approach was tested. Finally, our trained classification model achieved 87.6% accuracy in the prediction of drug-disease relation in cancer treatment and succeeded in discovering novel drug-disease relations that were actually reported in recent studies.
文摘保护定值的正确性对充分发挥继电保护系统的作用至关重要,但目前定值比对仍采用人工方式,工作量大、时间长且结果正确性无法保证。对此,梳理了定值名称的命名特点,提出了一种基于神经网络的继电保护定值名称智能比对方法。首先进行文本预处理,然后将预处理后的定值文本向量化,最后使用双向长短时记忆(bi-directional long short-term memory,Bi-LSTM)神经网络计算定值名称语义特征向量相似度。算例表明,基于神经网络的定值名称智能比对方法能有效完成定值单和运行定值名称的匹配,且神经网络比模糊匹配处理定值名称匹配问题准确率更高,速度更快。
