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Computational reconstruction of biol...

Yale University.

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Computational reconstruction of biological networks.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Computational reconstruction of biological networks./
Author:	Yip, Yuk Lap.
Description:	238 p.
Notes:	Advisers: Mark Gerstein; Drew McDermott.
Contained By:	Dissertation Abstracts International70-06B.
Subject:	Biology, Bioinformatics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3361633
ISBN:	9781109205442

Computational reconstruction of biological networks.
Yip, Yuk Lap.

Computational reconstruction of biological networks. - 238 p.

Advisers: Mark Gerstein; Drew McDermott.

Thesis (Ph.D.)--Yale University, 2009.

Networks describe the interactions between different objects. In living systems, knowing which biological objects interact with each other would deepen our understanding of the functions of both individual objects and their working modules. Due to experimental limitations, currently only small portions of these interaction networks are known. This thesis describes methods for computationally inferring the complete networks based on the known portions and related data. These methods exploit special data properties and problem structures to achieve high accuracy. The training set expansion method handles sparse and uneven training data by learning from information-rich regions of the network, and propagating the information to help learn from the information-poor regions. The multilevel learning framework combines information at different levels of a concept hierarchy, and lets the predictors at the different levels to propagate information between each other. Combined optimization between levels allow the integrated use of data features at different levels to improve prediction accuracy and noise immunity. Finally, proper incorporation of heterogeneous data facilitates the identification of interactions uniquely detectable by each kind of data. This thesis also describes some work on data integration and tool sharing, which are crucial components of network analysis studies.

ISBN: 9781109205442Subjects--Topical Terms:

1018415
Biology, Bioinformatics.

Computational reconstruction of biological networks.
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020 $a 9781109205442
035 $a (UMI)AAI3361633
035 $a AAI3361633
040 $a UMI $c UMI
100 1 $a Yip, Yuk Lap. $3 1044754
245 1 0 $a Computational reconstruction of biological networks.
300 $a 238 p.
500 $a Advisers: Mark Gerstein; Drew McDermott.
500 $a Source: Dissertation Abstracts International, Volume: 70-06, Section: B, page: .
502 $a Thesis (Ph.D.)--Yale University, 2009.
520 $a Networks describe the interactions between different objects. In living systems, knowing which biological objects interact with each other would deepen our understanding of the functions of both individual objects and their working modules. Due to experimental limitations, currently only small portions of these interaction networks are known. This thesis describes methods for computationally inferring the complete networks based on the known portions and related data. These methods exploit special data properties and problem structures to achieve high accuracy. The training set expansion method handles sparse and uneven training data by learning from information-rich regions of the network, and propagating the information to help learn from the information-poor regions. The multilevel learning framework combines information at different levels of a concept hierarchy, and lets the predictors at the different levels to propagate information between each other. Combined optimization between levels allow the integrated use of data features at different levels to improve prediction accuracy and noise immunity. Finally, proper incorporation of heterogeneous data facilitates the identification of interactions uniquely detectable by each kind of data. This thesis also describes some work on data integration and tool sharing, which are crucial components of network analysis studies.
590 $a School code: 0265.
650 4 $a Biology, Bioinformatics. $3 1018415
690 $a 0715
710 2 0 $a Yale University. $3 515640
773 0 $t Dissertation Abstracts International $g 70-06B.
790 $a 0265
790 1 0 $a Gerstein, Mark, $e advisor
790 1 0 $a McDermott, Drew, $e advisor
791 $a Ph.D.
792 $a 2009
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3361633