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Predicting protein molecular function.

University of California, Berkeley.

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Predicting protein molecular function.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Predicting protein molecular function./
Author:	Engelhardt, Barbara Elizabeth.
Description:	162 p.
Notes:	Adviser: Michael I. Jordan.
Contained By:	Dissertation Abstracts International69-05B.
Subject:	Artificial Intelligence. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3311662
ISBN:	9780549611257

Predicting protein molecular function.
Engelhardt, Barbara Elizabeth.

Predicting protein molecular function. - 162 p.

Adviser: Michael I. Jordan.

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

The number of known nucleotide sequences encoding proteins is growing at an extraordinarily fast rate due to technologies developed in the last decade that enable rapid sequence acquisition. Such rapid acquisition is a prelude to understanding the molecular function and tertiary structure of these protein sequences, and from there to an understanding of the role these proteins play in a particular organism. The experimental technologies that enable us to understand molecular function have not progressed as fast as those for sequencing. One role of computational biology is to accurately predict protein molecular function based on the protein's sequence alone.

ISBN: 9780549611257Subjects--Topical Terms:

769149
Artificial Intelligence.

Predicting protein molecular function.
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020 $a 9780549611257
035 $a (UMI)AAI3311662
035 $a AAI3311662
040 $a UMI $c UMI
100 1 $a Engelhardt, Barbara Elizabeth. $3 1019138
245 1 0 $a Predicting protein molecular function.
300 $a 162 p.
500 $a Adviser: Michael I. Jordan.
500 $a Source: Dissertation Abstracts International, Volume: 69-05, Section: B, page: 3079.
502 $a Thesis (Ph.D.)--University of California, Berkeley, 2007.
520 $a The number of known nucleotide sequences encoding proteins is growing at an extraordinarily fast rate due to technologies developed in the last decade that enable rapid sequence acquisition. Such rapid acquisition is a prelude to understanding the molecular function and tertiary structure of these protein sequences, and from there to an understanding of the role these proteins play in a particular organism. The experimental technologies that enable us to understand molecular function have not progressed as fast as those for sequencing. One role of computational biology is to accurately predict protein molecular function based on the protein's sequence alone.
520 $a Phylogenomics is a field of study that approaches the problem of protein molecular function prediction from an evolutionary perspective. In particular, a phylogenomic analysis transfers existing (but sparse) molecular function annotations to a query protein based on a reconciled phylogeny, which explicitly represents the evolutionary relationships of a set of related proteins. In my dissertation, I formalize the phylogenomics methodology as a statistical graphical model of molecular function evolution. Within this framework, we can predict protein molecular function from protein sequence alone. Molecular function evolution is represented as a simple continuous time Markov chain, and the random variables at each node in the tree are a subset of functional terms from the Gene Ontology. The model is encapsulated in a framework called SIFTER (Statistical Inference of Function Through Evolutionary Relationships).
520 $a SIFTER has performed well on a number of diverse protein families, as compared to standard annotation transfer methods and other phylogenomics-based approaches. SIFTER has been applied to the complete genomes of 46 fungal species, and is able to make molecular function predictions for a large percentage of the predicted proteins in these genomes. Moreover, through these predictions we can explore some genomic comparisons for fungi. Motivated by the high cost of characterization experiments, active learning techniques have also been applied to SIFTER's protein function predictions, with good results.
590 $a School code: 0028.
650 4 $a Artificial Intelligence. $3 769149
650 4 $a Biology, Bioinformatics. $3 1018415
650 4 $a Computer Science. $3 626642
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690 $a 0800
690 $a 0984
710 2 $a University of California, Berkeley. $3 687832
773 0 $t Dissertation Abstracts International $g 69-05B.
790 $a 0028
790 1 0 $a Jordan, Michael I., $e advisor
791 $a Ph.D.
792 $a 2007
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3311662