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Mining metastasis related genes by primary-secondary tumor comparisons from large-scale database
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Conference on Information and Knowledge Management archive
Proceeding of the 2nd international workshop on Data and text mining in bioinformatics table of contents
Napa Valley, California, USA
SESSION: Bio-data mining table of contents
Pages 29-36  
Year of Publication: 2008
ISBN:978-1-60558-251-1
Authors
Sangwoo Kim  KAIST, Daejeon, South Korea
Doheon Lee  KAIST, Daejeon, South Korea
Sponsors
SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web
SIGIR: ACM Special Interest Group on Information Retrieval
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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ABSTRACT

Metastasis is the most dangerous step in cancer progression and causes more than 90% of cancer death. Although many researchers have been working on biological features and characteristics of metastasis, most of its genetic level processes remain uncertain. Some studies succeeded in elucidating metastasis related genes and pathways, followed by predicting prognosis of cancer patients, but there still is a question whether the result genes or pathways contain enough information and noise features have been controlled appropriately. To address these problems, we conducted comparisons between primary tumors and secondary metastatic tumors. Noises from the differences of tissue specific characteristics between two types of tumors have been controlled by additional analyses. In this paper, we suggest a new method for identifying genes and pathways which secure metastasis dependency and are free of metastasis independent features.


REFERENCES

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