NDLI: TILD: A Strategy to Identify Cancer-related Genes Using Title Information in Literature Data

Content Provider	ACM Digital Library
Author	Yeu, Yunku Kim, Jeongwoo Kim, Hyunjin Shin, Mincheol Park, Sanghyun
Abstract	After genome project in 1990s, researches which are involved with gene have been progressed. These studies unearthed that gene is cause of disease, and relations between gene and disease are important. In this reason, we proposed a strategy called TILD that identifies cancer-related genes using title information in literature data. To implement our method, we selected cancer-specific literature data from the online database. We then extracted genes using text mining. In the next step, we classified into two kinds for extracted genes using title information. If genes are located in title, then they are classified as hub genes. In the contrast, if genes are located in body, then they are classified as sub genes which are connected with hub genes. We iterated the processes for each paper to construct the cancer-specific local gene network. In the last step, we constructed global cancer-specific gene network by integrating all local gene network, and calculated a score for each gene based on analysis of the global gene network. We assumed that genes in title have meaningful relations with cancer, and other genes in the body are related with the title genes. For validation, we compared with other methods for the top 20 genes inferred by each approach. Our approach found more cancer-related genes than comparable methods.
Starting Page	51
Ending Page	51
Page Count	1
File Format	PDF
ISBN	9781450312752
DOI	10.1145/2665970.2665992
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-11-07
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Relation Gene Text-mining Analysis Network Cancer
Content Type	Text
Resource Type	Article

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