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Learning genetic regulatory network ...

Barker, Nathan.

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Learning genetic regulatory network connectivity from time series data.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Learning genetic regulatory network connectivity from time series data./
Author:	Barker, Nathan.
Description:	151 p.
Notes:	Source: Dissertation Abstracts International, Volume: 68-09, Section: B, page: 6059.
Contained By:	Dissertation Abstracts International68-09B.
Subject:	Biology, Bioinformatics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3282758
ISBN:	9780549245698

Learning genetic regulatory network connectivity from time series data.
Barker, Nathan.

Learning genetic regulatory network connectivity from time series data. - 151 p.

Source: Dissertation Abstracts International, Volume: 68-09, Section: B, page: 6059.

Thesis (Ph.D.)--The University of Utah, 2007.

Recent experimental advances facilitate the collection of time series data that indicate which genes in a cell are expressed. This information can be used to understand the genetic regulatory network that generates the data. Typically, Bayesian analysis approaches are applied which neglect the time series nature of the experimental data have difficulty in determining the direction of causality, and do not perform well on networks with tight feedback.

ISBN: 9780549245698Subjects--Topical Terms:

1018415
Biology, Bioinformatics.

Learning genetic regulatory network connectivity from time series data.
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020 $a 9780549245698
035 $a (UMI)AAI3282758
035 $a AAI3282758
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100 1 $a Barker, Nathan. $3 1269516
245 1 0 $a Learning genetic regulatory network connectivity from time series data.
300 $a 151 p.
500 $a Source: Dissertation Abstracts International, Volume: 68-09, Section: B, page: 6059.
502 $a Thesis (Ph.D.)--The University of Utah, 2007.
520 $a Recent experimental advances facilitate the collection of time series data that indicate which genes in a cell are expressed. This information can be used to understand the genetic regulatory network that generates the data. Typically, Bayesian analysis approaches are applied which neglect the time series nature of the experimental data have difficulty in determining the direction of causality, and do not perform well on networks with tight feedback.
520 $a To address these problems, this dissertation presents an improved method, called the GeneNet algorithm, to learn genetic regulatory network connectivity which exploits the time series nature of experimental data to allow for better causal predictions on networks with tight feedback. More specifically, the GeneNet algorithm provides several contributions to the area of genetic network discovery. It finds networks with cyclic or tight feedback behavior often missed by other methods as it performs a more local analysis of the data. It provides the researcher with the ability to see the interactions between genes in a genetic network. It guides experimental design by providing feedback to the researcher as to which parts of the network are the most unclear. It is encased in an infrastructure that allows for rapid genetic network model creation and evaluation.
520 $a The GeneNet algorithm first encodes the data into levels. Next, it determines an initial set of influence vectors for each species based upon the probability of the species' expression increasing. From this set of influence vectors, it determines if any influence vectors should be merged, representing a combined effect. Finally, influence vectors are competed against each other to obtain the best influence vector. The result is a directed graph representation of the genetic network's repression and activation connections. Results are reported for several synthetic networks showing significant improvements in both recall and runtime while performing nearly as well or better in precision over a dynamic Bayesian approach.
590 $a School code: 0240.
650 4 $a Biology, Bioinformatics. $3 1018415
650 4 $a Computer Science. $3 626642
690 $a 0715
690 $a 0984
710 2 $a The University of Utah. $3 1017410
773 0 $t Dissertation Abstracts International $g 68-09B.
790 $a 0240
791 $a Ph.D.
792 $a 2007
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3282758