NDLI: LIF: a method to infer disease-gene relationships using literature data and impact factor

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LIF: a method to infer disease-gene relationships using literature data and impact factor

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LIF: a method to infer disease-gene relationships using literature data and impact factor

Content Provider	ACM Digital Library
Author	Kim, Chunghun Lee, Jinyoung Kim, Jeongwoo Kang, Heechul Park, Sanghyun
Abstract	Biological relationships are important in discovering the causes of disease. Therefore, a number of studies have been conducted to extract information regarding the relationships between biological entities. However, given the large number of journals and amount of literature that is available, it is difficult to assess data regarding biological relationships. In this study, we present a method called LIF, which infers disease-gene relationships using literature data and impact factor. Since the impact factor is influenced by a large number of researchers, we considered that the impact factor can be used as a measure to evaluate relationships that are extracted from literature data. To implement the LIF method, we extracted genes from disease-specific literature data. We then calculated the weight of the genes based on the impact factor of the literature in which the genes were described. For validation, we investigated the top N inferred genes for lung cancer, using an answer set. The answer set comprised several databases that contained information on disease-gene relationships. We demonstrated that the LIF is a useful method to infer disease-gene relationships compared with existing methods.
Starting Page	3
Ending Page	10
Page Count	8
File Format	PDF
ISBN	9781450344869
DOI	10.1145/3019612.3019613
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-04-03
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Disease Gene Text-mining Impact factor
Content Type	Text
Resource Type	Article

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