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Devising effective similarity measur...

Chikina, Maria.

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Devising effective similarity measures and learning algorithms for the study of metazoan gene expression.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Devising effective similarity measures and learning algorithms for the study of metazoan gene expression./
Author:	Chikina, Maria.
Description:	124 p.
Notes:	Source: Dissertation Abstracts International, Volume: 72-04, Section: B, page: 1865.
Contained By:	Dissertation Abstracts International72-04B.
Subject:	Biology, Molecular. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3445566
ISBN:	9781124491882

Devising effective similarity measures and learning algorithms for the study of metazoan gene expression.
Chikina, Maria.

Devising effective similarity measures and learning algorithms for the study of metazoan gene expression. - 124 p.

Source: Dissertation Abstracts International, Volume: 72-04, Section: B, page: 1865.

Thesis (Ph.D.)--Princeton University, 2011.

With the advent of genome sequencing and modern high-throughput technologies, there is an increasing need for effective and robust methods to turn terabytes of data into biological knowledge. Every new data type and biological problem presents unique analysis challenges; this work focuses on several problems specific to metazoan gene expression.

ISBN: 9781124491882Subjects--Topical Terms:

1017719
Biology, Molecular.

Devising effective similarity measures and learning algorithms for the study of metazoan gene expression.
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020 $a 9781124491882
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040 $a UMI $c UMI
100 1 $a Chikina, Maria. $3 1682244
245 1 0 $a Devising effective similarity measures and learning algorithms for the study of metazoan gene expression.
300 $a 124 p.
500 $a Source: Dissertation Abstracts International, Volume: 72-04, Section: B, page: 1865.
500 $a Adviser: Olga Troyanskaya.
502 $a Thesis (Ph.D.)--Princeton University, 2011.
520 $a With the advent of genome sequencing and modern high-throughput technologies, there is an increasing need for effective and robust methods to turn terabytes of data into biological knowledge. Every new data type and biological problem presents unique analysis challenges; this work focuses on several problems specific to metazoan gene expression.
520 $a Methods for analyzing high dimensional data fall into two broad classes, unsupervised and supervised. Unsupervised approaches characterize the components of a dataset without any a priori input while supervised methods rely on existing knowledge to find predictive patterns within datasets. In this work we develop and apply methods that span a range of data analysis techniques. Chapters 2 and 3 concern supervised methods for predicting tissue expression and tissue-specific interactions in C. elegans. We first apply and analyze a well characterized method, Support Vector Machines, to predict tissue-specific expression, and then extend it to two novel methods that allow us to address more complex problems. In the next two chapters we develop two unsupervised methods that address the question of how to define biologically meaningful similarity metrics: we present a powerful yet transparent method to analyze the functional similarity of genes across species and a statistically rigorous method to compare ChIPseq experiments, a relatively new technique that produces complexly structured data.
590 $a School code: 0181.
650 4 $a Biology, Molecular. $3 1017719
650 4 $a Biology, Bioinformatics. $3 1018415
650 4 $a Computer Science. $3 626642
690 $a 0307
690 $a 0715
690 $a 0984
710 2 $a Princeton University. $3 645579
773 0 $t Dissertation Abstracts International $g 72-04B.
790 1 0 $a Troyanskaya, Olga, $e advisor
790 $a 0181
791 $a Ph.D.
792 $a 2011
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3445566