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 cancerspecific 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 cancerspecific 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.
|Title of host publication||DTMBIO 2014 - Proceedings of the ACM 8th International Workshop on Data and Text Mining in Bioinformatics, co-located with CIKM 2014|
|Publisher||Association for Computing Machinery, Inc|
|Number of pages||1|
|Publication status||Published - 2014 Nov 7|
|Event||8th ACM International Workshop on Data and Text Mining in Biomedical Informatics, DTMBIO 2014 - Shanghai, China|
Duration: 2014 Nov 7 → …
|Name||DTMBIO 2014 - Proceedings of the ACM 8th International Workshop on Data and Text Mining in Bioinformatics, co-located with CIKM 2014|
|Other||8th ACM International Workshop on Data and Text Mining in Biomedical Informatics, DTMBIO 2014|
|Period||14/11/7 → …|
Bibliographical noteFunding Information:
The National Science Foundation, University of Missouri Molecular Biology Program, and the Department of Molecular Microbiology and Immunology supported this work. We thank Kelly Tatchell, Raad Gitan, Mark Johnston, and Wolfgang Seufert for plasmids and strains.
All Science Journal Classification (ASJC) codes
- Computational Theory and Mathematics
- Computer Science Applications