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.
|Title of host publication||32nd Annual ACM Symposium on Applied Computing, SAC 2017|
|Publisher||Association for Computing Machinery|
|Number of pages||8|
|Publication status||Published - 2017 Apr 3|
|Event||32nd Annual ACM Symposium on Applied Computing, SAC 2017 - Marrakesh, Morocco|
Duration: 2017 Apr 4 → 2017 Apr 6
|Name||Proceedings of the ACM Symposium on Applied Computing|
|Other||32nd Annual ACM Symposium on Applied Computing, SAC 2017|
|Period||17/4/4 → 17/4/6|
Bibliographical noteFunding Information:
This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIP) (NRF-2015R1A2A1A05001845).
© 2017 ACM.
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