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New approaches to finding biomarkers...

Dalgin, Gul Safak.

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New approaches to finding biomarkers: Application to renal clear-cell carcinoma and breast cancer.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	New approaches to finding biomarkers: Application to renal clear-cell carcinoma and breast cancer./
作者:	Dalgin, Gul Safak.
面頁冊數:	176 p.
附註:	Adviser: Charles DeLisi.
Contained By:	Dissertation Abstracts International69-01B.
標題:	Biology, Biostatistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3298621
ISBN:	9780549427353

New approaches to finding biomarkers: Application to renal clear-cell carcinoma and breast cancer.
Dalgin, Gul Safak.

New approaches to finding biomarkers: Application to renal clear-cell carcinoma and breast cancer. - 176 p.

Adviser: Charles DeLisi.

Thesis (Ph.D.)--Boston University, 2008.

Elucidating the molecular mechanisms responsible for over- or under-expression of genes poses another challenge. This is critically important for developing effective diagnostic assays and designing customized therapeutics.

ISBN: 9780549427353Subjects--Topical Terms:

1018416
Biology, Biostatistics.

New approaches to finding biomarkers: Application to renal clear-cell carcinoma and breast cancer.
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100 1 $a Dalgin, Gul Safak. $3 1263119
245 1 0 $a New approaches to finding biomarkers: Application to renal clear-cell carcinoma and breast cancer.
300 $a 176 p.
500 $a Adviser: Charles DeLisi.
500 $a Source: Dissertation Abstracts International, Volume: 69-01, Section: B, page: 0034.
502 $a Thesis (Ph.D.)--Boston University, 2008.
520 $a Elucidating the molecular mechanisms responsible for over- or under-expression of genes poses another challenge. This is critically important for developing effective diagnostic assays and designing customized therapeutics.
520 $a Microarray gene-expression profiling has emerged as a powerful tool to identify specific tumor markers that distinguish tumor from the normal phenotype. A key problem, however, is that gene sets are currently too large to use as economically viable diagnostic tools. Moreover, individual markers do not distinguish tumor samples from normal controls with 100% accuracy, although the set as a whole might do so.
520 $a This thesis presents a novel method for cancer diagnosis that employs an exhaustive search to identify predictive single genes and gene pairs. Application to renal cell carcinoma (RCC) identified 158 single markers each having 100% coverage and accuracy. This set of genes represents a great improvement with respect to the initial analysis of this dataset because the number of markers is reduced ten-fold and the set is three-fold more enriched with cancer-related genes.
520 $a This thesis also explores regulatory mechanisms for the candidate marker genes. First, we computationally selected down-regulated biomarkers that can be hypermethylated in RCC. Analysis by MALDI-TOF mass spectrometry revealed seven significantly over-methylated regions from six markers, five of which are reported for the first time to be over-methylated in RCC. In a second approach, we used various statistical approaches to identify known transcription factors and predict new ones.
520 $a When a single gene expression failed to distinguish cancer samples from normal controls, our method identified gene pairs for RCC and breast cancer separately. Moreover, for breast cancer, a collaboration with researchers at IBM revealed robustness of breast cancer subtypes through consensus hierarchical clustering. This technique also identified six new subtypes, each of which progresses from the pre-invasive to the invasive stage through a different pathway.
520 $a The biomarkers presented here constitute good candidates for diagnostic assays for RCC and breast cancer. This thesis demonstrates that high-throughput computational methods based on rigorous statistics can greatly enhance biomarker discovery. The framework used can provide insight into developing effective tools for diagnostics---and ultimately therapeutic---for different cancer types.
590 $a School code: 0017.
650 4 $a Biology, Biostatistics. $3 1018416
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