東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Novel techniques for early detection...

University of Minnesota.

FindBook

Google Book

Amazon

博客來

Novel techniques for early detection of ovarian carcinoma.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Novel techniques for early detection of ovarian carcinoma./
作者:	Tchagang, Alain Beaudelaire.
面頁冊數:	185 p.
附註:	Adviser: Ahmed H. Tewfik.
Contained By:	Dissertation Abstracts International68-07B.
標題:	Biology, Bioinformatics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3273165
ISBN:	9780549116691

Novel techniques for early detection of ovarian carcinoma.
Tchagang, Alain Beaudelaire.

Novel techniques for early detection of ovarian carcinoma. - 185 p.

Adviser: Ahmed H. Tewfik.

Thesis (Ph.D.)--University of Minnesota, 2007.

Motivation. Despite advances in surgery and chemotherapy, ovarian cancer is still the most lethal form of gynecologic cancer and the fourth leading cause of cancer death among women in developed countries. One reason it is so deadly is the fact that ovarian cancer is not usually diagnosed until it has reached an advanced stage. Early detection can help prolong or save lives, but clinicians currently have no specific and sensitive screening method because the disease shows very subtle symptoms. In this work, we propose and study a novel approach for group-biomarkers as an alternative to the traditional single-biomarkers and other combination of biomarkers used to date or proposed in the literature for the diagnostic of early stage and/or recurrence of ovarian cancer using a blood test.

ISBN: 9780549116691Subjects--Topical Terms:

1018415
Biology, Bioinformatics.

Novel techniques for early detection of ovarian carcinoma.
LDR:03110nam 2200301 a 45 001 861677
005 20100720
008 100720s2007 ||||||||||||||||| ||eng d
020 $a 9780549116691
035 $a (UMI)AAI3273165
035 $a AAI3273165
040 $a UMI $c UMI
100 1 $a Tchagang, Alain Beaudelaire. $3 1029399
245 1 0 $a Novel techniques for early detection of ovarian carcinoma.
300 $a 185 p.
500 $a Adviser: Ahmed H. Tewfik.
500 $a Source: Dissertation Abstracts International, Volume: 68-07, Section: B, page: 4644.
502 $a Thesis (Ph.D.)--University of Minnesota, 2007.
520 $a Motivation. Despite advances in surgery and chemotherapy, ovarian cancer is still the most lethal form of gynecologic cancer and the fourth leading cause of cancer death among women in developed countries. One reason it is so deadly is the fact that ovarian cancer is not usually diagnosed until it has reached an advanced stage. Early detection can help prolong or save lives, but clinicians currently have no specific and sensitive screening method because the disease shows very subtle symptoms. In this work, we propose and study a novel approach for group-biomarkers as an alternative to the traditional single-biomarkers and other combination of biomarkers used to date or proposed in the literature for the diagnostic of early stage and/or recurrence of ovarian cancer using a blood test.
520 $a Methods. We identify group-biomarkers by applying a novel set of biclustering algorithms that we propose and develop on a set of well defined gene expression data representing 62 normal ovary tissues, 7 borderline ovarian cancer tissues, 22 papillary serous adenocarcinoma of ovarian tumors, 16 omentum papillary serous adenocarcinoma of ovarian tumors, and 319 non-ovarian healthy and diseased tissues coming from different parts of the body. We further validate the group-biomarkers uncovered in this study using a publicly available set of ovarian cancer data downloaded from the NIH website.
520 $a Results. We identified many significant patterns that encode for secreted proteins that clearly discriminate between the gene expression data of ovarian cancer tissues, normal ovary tissues, and non-ovarian tissues, among which three candidate group-biomarkers that exhibit a suitable and conserved biological pattern that may be used for early detection or recurrence of ovarian cancer with specificity greater than 99% and sensitivity equal to 100% with 99% accuracy. We also present a statistical analysis that demonstrates that our methodology for identifying potential group -biomarkers have a much better detection performance than other techniques proposed in the literature. In other words, our methodology identifies the optimum combination of genes that have the highest impact on the diagnostic of a disease.
590 $a School code: 0130.
650 4 $a Biology, Bioinformatics. $3 1018415
650 4 $a Engineering, Biomedical. $3 1017684
690 $a 0541
690 $a 0715
710 2 $a University of Minnesota. $3 676231
773 0 $t Dissertation Abstracts International $g 68-07B.
790 $a 0130
790 1 0 $a Tewfik, Ahmed H., $e advisor
791 $a Ph.D.
792 $a 2007
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3273165