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Statistical mechanical approaches to...

University of California, Berkeley.

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Statistical mechanical approaches to informatics: Discovering motifs and clustering data.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Statistical mechanical approaches to informatics: Discovering motifs and clustering data./
Author:	Hendrix, David Anthony.
Description:	154 p.
Notes:	Adviser: Daniel Rokhsar.
Contained By:	Dissertation Abstracts International68-08B.
Subject:	Biology, Bioinformatics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3275445
ISBN:	9780549168317

Statistical mechanical approaches to informatics: Discovering motifs and clustering data.
Hendrix, David Anthony.

Statistical mechanical approaches to informatics: Discovering motifs and clustering data. - 154 p.

Adviser: Daniel Rokhsar.

Thesis (Ph.D.)--University of California, Berkeley, 2007.

The problems of Informatics, of organizing, modeling, and finding patterns in immense amounts of data, are central to much of modern science. This is especially true in the fields of Biology and Bioinformatics, where the amount data in the form of genomes, proteomes, and microarray data grows faster every day. Central to these problems are the specific tasks of finding motifs present in a collection of data, and in clustering data into meaningful groups. In the motif finding problem, one is presented with a collection of data that contains instances of a pattern throughout and must find this pattern without knowledge of what it looks like. In the problem of clustering data, one is presented with a collection of data and is asked to group this data into meaningful groups. This thesis will take the approaches of Statistical Mechanics, and develop methodologies to study these issues, and apply them to real biological data sets. In particular, the general problem of DNA motif finding will be addressed, as well as specific motif finding problems in Core Promoters and cis-regulatory enhancers. In addition, we will briefly examine the problem of clustering genes by their textual annotation.

ISBN: 9780549168317Subjects--Topical Terms:

1018415
Biology, Bioinformatics.

Statistical mechanical approaches to informatics: Discovering motifs and clustering data.
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100 1 $a Hendrix, David Anthony. $3 1018647
245 1 0 $a Statistical mechanical approaches to informatics: Discovering motifs and clustering data.
300 $a 154 p.
500 $a Adviser: Daniel Rokhsar.
500 $a Source: Dissertation Abstracts International, Volume: 68-08, Section: B, page: 4923.
502 $a Thesis (Ph.D.)--University of California, Berkeley, 2007.
520 $a The problems of Informatics, of organizing, modeling, and finding patterns in immense amounts of data, are central to much of modern science. This is especially true in the fields of Biology and Bioinformatics, where the amount data in the form of genomes, proteomes, and microarray data grows faster every day. Central to these problems are the specific tasks of finding motifs present in a collection of data, and in clustering data into meaningful groups. In the motif finding problem, one is presented with a collection of data that contains instances of a pattern throughout and must find this pattern without knowledge of what it looks like. In the problem of clustering data, one is presented with a collection of data and is asked to group this data into meaningful groups. This thesis will take the approaches of Statistical Mechanics, and develop methodologies to study these issues, and apply them to real biological data sets. In particular, the general problem of DNA motif finding will be addressed, as well as specific motif finding problems in Core Promoters and cis-regulatory enhancers. In addition, we will briefly examine the problem of clustering genes by their textual annotation.
590 $a School code: 0028.
650 4 $a Biology, Bioinformatics. $3 1018415
650 4 $a Physics, Molecular. $3 1018648
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773 0 $t Dissertation Abstracts International $g 68-08B.
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791 $a Ph.D.
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