WEKO3
インデックスリンク
アイテム
Detection of network structure changes by graphical chain modeling: A case study of hepatitis C virus-related hepatocellular carcinoma
http://hdl.handle.net/2297/24279
http://hdl.handle.net/2297/24279275886a2-fd08-4f74-bc2b-008f239b9017
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
ME-PR-KANEKO-S-5624.pdf (782.8 kB)
|
|
| Item type | 会議発表論文 / Conference Paper(1) | |||||
|---|---|---|---|---|---|---|
| 公開日 | 2017-10-03 | |||||
| タイトル | ||||||
| タイトル | Detection of network structure changes by graphical chain modeling: A case study of hepatitis C virus-related hepatocellular carcinoma | |||||
| 言語 | ||||||
| 言語 | eng | |||||
| 資源タイプ | ||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_5794 | |||||
| 資源タイプ | conference paper | |||||
| 著者 |
Saito, Shigeru
× Saito, Shigeru× Honda, Masao× Kaneko, Shuichi× Horimoto, Katsuhisa |
|||||
| 書誌情報 |
Proceedings of the IEEE Conference on Decision and Control 号 5400061, p. 5624-5630, 発行日 2009-01-01 |
|||||
| ISSN | ||||||
| 収録物識別子タイプ | ISSN | |||||
| 収録物識別子 | 0191-2216 | |||||
| NCID | ||||||
| 収録物識別子タイプ | NCID | |||||
| 収録物識別子 | AA10474572 | |||||
| DOI | ||||||
| 関連タイプ | isIdenticalTo | |||||
| 識別子タイプ | DOI | |||||
| 関連識別子 | 10.1109/CDC.2009.5400061 | |||||
| 出版者 | ||||||
| 出版者 | IEEE = Institute of Electrical and Electronics Engineers | |||||
| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | One of the most characteristic features of biological molecular networks is that the network structure itself changes, depending on the cellular environment. Indeed, activated molecules show a variety of responses to distinctive cell conditions, and subsequently the network structures of active molecules also change. Here we present an approach to trace the network structure changes by using the graphical chain model developed from the gene expression data. The previous procedure for applying the graphical chain model to the expression profiles of a limited number of genes has been improved to analyze the entire set of genes. Furthermore, the chain model has been rearranged according to the association strength, and was scrutinized to identify the candidates of essential gene-gene relationships for the network changes, by using the path consistency algorithm. The improved procedure was applied to the expression profiles of 8,427 genes, which were measured in two distinctive stages of liver cancer progression. As a result, the chain model of the 18 gene cluster relationships with strong associations was inferred, in which the coordination of clusters was described in the cell stage progression, and the gene-gene relationships between known cancer-related genes causing the progression were further refined. Thus, the present procedure is a useful method to model the network structure changes in the cell stage progression, and to clarify the gene candidates for the progression. ©2009 IEEE. | |||||
| 著者版フラグ | ||||||
| 出版タイプ | VoR | |||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||
