【摘 要】
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We present a novel text mining approach to uncover the functional gene relationships, maybe, temporal and spatial functional modular interaction networks, from MEDLINE in large scale.Other than the re
【机 构】
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College of Computer Science,Zhejiang University,Hangzhou,310027,P.R.China
【出 处】
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”数字化中医信息系统“临床术语本体研究专家研讨会
论文部分内容阅读
We present a novel text mining approach to uncover the functional gene relationships, maybe, temporal and spatial functional modular interaction networks, from MEDLINE in large scale.Other than the regular approaches,which only consider the reductionistic molecular biological knowledge in MEDLINE, we use TCM knowledge(e.g.Symptom Complex) and the 50,000TCM bibliographic records to automatically congregate the related genes.A simple but efficient bootstrapping technique is used to extract the clinical disease names from TCM literature, and term co-occurrence is used to identify the disease-gene relationships in MEDLINE abstracts and titles.The underlying hypothesis is that the relevant genes of the same Symptom Complex will have some biological interactions.It is also a probing research to study the connection of TCM with modem biomedical and post-genomics studies by text mining.The preliminary results show that Symptom Complex gives a novel top-down view of functional genomics research, and it is a promising research field while connecting TCM with modem life science using text mining.
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