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Statistical approaches and rich prob...

New York University., Biology.

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Statistical approaches and rich probabilistic models of biological regulation.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Statistical approaches and rich probabilistic models of biological regulation./
Author:	Cheng, Fang.
Description:	183 p.
Notes:	Adviser: Bhubaneswar Mishra.
Contained By:	Dissertation Abstracts International68-09B.
Subject:	Biology, Bioinformatics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3283376
ISBN:	9780549257417

Statistical approaches and rich probabilistic models of biological regulation.
Cheng, Fang.

Statistical approaches and rich probabilistic models of biological regulation. - 183 p.

Adviser: Bhubaneswar Mishra.

Thesis (Ph.D.)--New York University, 2007.

Understanding biological regulation is a critical step towards our understanding of developmental and disease processes. As tremendous progress has been made in experimental technologies for sequencing the genetic code (DNA sequencing) and for quantitatively monitoring the level of expression over a developmental process in a high-throughput fashion, we are facing a great era for using computational power to extract the information that can fill up the gaps between the genetic code and the phenotypic changes, which involves the understanding of transcriptional regulation, post-transcriptional regulation, and regulatory relationship among molecular processes.

ISBN: 9780549257417Subjects--Topical Terms:

1018415
Biology, Bioinformatics.

Statistical approaches and rich probabilistic models of biological regulation.
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020 $a 9780549257417
035 $a (UMI)AAI3283376
035 $a AAI3283376
040 $a UMI $c UMI
100 1 $a Cheng, Fang. $3 1029436
245 1 0 $a Statistical approaches and rich probabilistic models of biological regulation.
300 $a 183 p.
500 $a Adviser: Bhubaneswar Mishra.
500 $a Source: Dissertation Abstracts International, Volume: 68-09, Section: B, page: 5665.
502 $a Thesis (Ph.D.)--New York University, 2007.
520 $a Understanding biological regulation is a critical step towards our understanding of developmental and disease processes. As tremendous progress has been made in experimental technologies for sequencing the genetic code (DNA sequencing) and for quantitatively monitoring the level of expression over a developmental process in a high-throughput fashion, we are facing a great era for using computational power to extract the information that can fill up the gaps between the genetic code and the phenotypic changes, which involves the understanding of transcriptional regulation, post-transcriptional regulation, and regulatory relationship among molecular processes.
520 $a This dissertation is dedicated to the construction of probabilistic models that can realistically capture the properties of several specific types of the biological regulation, and the development of statistical or computational approaches that can "learn" these models from the currently available experimental data. In particular: (i) a novel approach named "Mixed sample Processes Enrichment Analysis" (MixPEA) that deconvolves cell heterogeneity to identify molecular processes involved in a developmental program. An application of MixPEA in breast epithelial morphogenesis uncovered promising biological hypothesis regarding the critical processes/pathways and their contributions to this morphogenetic program. (ii) the first statistical learning based approach in automatic hypothesis inference of alternative splicing regulation from microarray-based splicing detection data. The approach distinguishes itself from the alternative methods with the ability to learn the regulatory module and the cis-regulatory code at the same time, and to integrating multi-level regulation related information to form a rich definition of the cis-regulatory information. (iii) a computational study of the transcriptional regulatory role of transcriptional factor, P63, which suggested P63's role as a regulator of an adhesion program in epithelial cells. (iv) a systematic study on how choice of sample and control groups affects the performance of motif finding algorithm.
590 $a School code: 0146.
650 4 $a Biology, Bioinformatics. $3 1018415
690 $a 0715
710 2 $a New York University. $b Biology. $3 1029435
773 0 $t Dissertation Abstracts International $g 68-09B.
790 $a 0146
790 1 0 $a Mishra, Bhubaneswar, $e advisor
791 $a Ph.D.
792 $a 2007
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3283376