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Accurate prediction of causative pro...

Torkamani, Ali.

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Accurate prediction of causative protein kinase polymorphisms in inherited disease and cancer.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Accurate prediction of causative protein kinase polymorphisms in inherited disease and cancer./
Author:	Torkamani, Ali.
Description:	293 p.
Notes:	Adviser: Nicholas J. Schork.
Contained By:	Dissertation Abstracts International69-01B.
Subject:	Biology, Bioinformatics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3296787
ISBN:	9780549423270

Accurate prediction of causative protein kinase polymorphisms in inherited disease and cancer.
Torkamani, Ali.

Accurate prediction of causative protein kinase polymorphisms in inherited disease and cancer. - 293 p.

Adviser: Nicholas J. Schork.

Thesis (Ph.D.)--University of California, San Diego, 2008.

Understanding the genetic basis of disease is important, not only, for understanding the molecular mechanisms driving a particular disease phenotype, but also for providing informative prognostic, and diagnostic markers, as well as allowing for the design of personalized therapeutic intervention. Identifying these causative genetic variants is a complex problem because of the relatively small level of risk some variants may contribute, the interplay of variants which may be neutral in isolation, population stratification in purely statistical identification of risk variants, and the overwhelming number of neutral variants present in any individuals genome or tumor genome. A number of computational methods for prioritization of risk factors have been developed, each with a large weakness due to efforts to form generalized predictions. In this dissertation, I describe a specialized prediction method, tailored towards identification of causative polymorphisms in the protein kinase gene family, and demonstrate its applicability to identification of polymorphisms involved in inherited disease as well as cancer. Chapter 1 describes the method itself, Chapter 2 describes its applicability to cancer, and Chapters 3 and 4 delve into further details of the contributions of some of the predictive attributes.

ISBN: 9780549423270Subjects--Topical Terms:

1018415
Biology, Bioinformatics.

Accurate prediction of causative protein kinase polymorphisms in inherited disease and cancer.
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020 $a 9780549423270
035 $a (UMI)AAI3296787
035 $a AAI3296787
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100 1 $a Torkamani, Ali. $3 1263122
245 1 0 $a Accurate prediction of causative protein kinase polymorphisms in inherited disease and cancer.
300 $a 293 p.
500 $a Adviser: Nicholas J. Schork.
500 $a Source: Dissertation Abstracts International, Volume: 69-01, Section: B, page: 0082.
502 $a Thesis (Ph.D.)--University of California, San Diego, 2008.
520 $a Understanding the genetic basis of disease is important, not only, for understanding the molecular mechanisms driving a particular disease phenotype, but also for providing informative prognostic, and diagnostic markers, as well as allowing for the design of personalized therapeutic intervention. Identifying these causative genetic variants is a complex problem because of the relatively small level of risk some variants may contribute, the interplay of variants which may be neutral in isolation, population stratification in purely statistical identification of risk variants, and the overwhelming number of neutral variants present in any individuals genome or tumor genome. A number of computational methods for prioritization of risk factors have been developed, each with a large weakness due to efforts to form generalized predictions. In this dissertation, I describe a specialized prediction method, tailored towards identification of causative polymorphisms in the protein kinase gene family, and demonstrate its applicability to identification of polymorphisms involved in inherited disease as well as cancer. Chapter 1 describes the method itself, Chapter 2 describes its applicability to cancer, and Chapters 3 and 4 delve into further details of the contributions of some of the predictive attributes.
590 $a School code: 0033.
650 4 $a Biology, Bioinformatics. $3 1018415
650 4 $a Biology, Genetics. $3 1017730
650 4 $a Health Sciences, Epidemiology. $3 1019544
690 $a 0369
690 $a 0715
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710 2 $a University of California, San Diego. $b Biomedical Sciences. $3 1021780
773 0 $t Dissertation Abstracts International $g 69-01B.
790 $a 0033
790 1 0 $a Desai, Arshad $e committee member
790 1 0 $a Manning, Gerard $e committee member
790 1 0 $a Newton, Alexandra $e committee member
790 1 0 $a Schork, Nicholas J., $e advisor
790 1 0 $a Taylor, Susan S. $e committee member
790 1 0 $a Wynshaw-Boris, Anthony $e committee member
791 $a Ph.D.
792 $a 2008
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3296787