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Mining Comparative Genomic Hybridiza...

University of Florida.

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Mining Comparative Genomic Hybridization data.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Mining Comparative Genomic Hybridization data./
作者:	Liu, Jun.
面頁冊數:	128 p.
附註:	Advisers: Sanjay Ranka; Tamer Kahveci.
Contained By:	Dissertation Abstracts International69-07B.
標題:	Biology, Bioinformatics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3322933
ISBN:	9780549728214

Mining Comparative Genomic Hybridization data.
Liu, Jun.

Mining Comparative Genomic Hybridization data. - 128 p.

Advisers: Sanjay Ranka; Tamer Kahveci.

Thesis (Ph.D.)--University of Florida, 2008.

Numerical and structural chromosomal imbalances are one of the most prominent features of neoplastic cells. Thousands of (molecular-) cytogenetic studies of human neoplasias have searched for insights into genetic mechanisms of tumor development and the detection of targets for pharmacologic intervention. It is assumed that repetitive chromosomal aberration patterns reflect the supposed cooperation of a multitude of tumor relevant genes in most malignant diseases.

ISBN: 9780549728214Subjects--Topical Terms:

1018415
Biology, Bioinformatics.

Mining Comparative Genomic Hybridization data.
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500 $a Advisers: Sanjay Ranka; Tamer Kahveci.
500 $a Source: Dissertation Abstracts International, Volume: 69-07, Section: B, page: 4264.
502 $a Thesis (Ph.D.)--University of Florida, 2008.
520 $a Numerical and structural chromosomal imbalances are one of the most prominent features of neoplastic cells. Thousands of (molecular-) cytogenetic studies of human neoplasias have searched for insights into genetic mechanisms of tumor development and the detection of targets for pharmacologic intervention. It is assumed that repetitive chromosomal aberration patterns reflect the supposed cooperation of a multitude of tumor relevant genes in most malignant diseases.
520 $a One method for measuring genomic aberrations is Comparative Genomic Hybridization (CGH). CGH is a molecular-cytogenetic analysis method for detecting regions with genomic imbalances (gains or losses of DNA segments). CGH data of an individual tumor can be considered as an ordered list of discrete values, where each value corresponds to a single chromosomal band and denotes one of three aberration statuses (gain, loss and no change). Along with the high dimensionality (around 1000), a key feature of the CGH data is that consecutive values are highly correlated.
520 $a In this research, we have developed novel data mining methods to exploit these characteristics. We have developed novel algorithms for feature selection, clustering and classification of CGH data sets consisting of samples from multiple cancer types. We have also developed novel methods and models for understanding the progression of cancer. Experimental results on real CGH datasets show the benefits of our methods as compared to existing methods in the literature.
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