東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Devising effective similarity measur...

Chikina, Maria.

FindBook

Google Book

Amazon

博客來

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

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Devising effective similarity measures and learning algorithms for the study of metazoan gene expression./
作者:	Chikina, Maria.
面頁冊數:	124 p.
附註:	Source: Dissertation Abstracts International, Volume: 72-04, Section: B, page: 1865.
Contained By:	Dissertation Abstracts International72-04B.
標題:	Biology, Molecular. -
電子資源:	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.
LDR:02404nam 2200301 4500 001 1403012
005 20111108080349.5
008 130515s2011 ||||||||||||||||| ||eng d
020 $a 9781124491882
035 $a (UMI)AAI3445566
035 $a AAI3445566
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