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Algorithms, Data Structures, and Statistical Analyses for Improving Chemical Database Searches.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Algorithms, Data Structures, and Statistical Analyses for Improving Chemical Database Searches./
Author:	Nasr, Ramzi J.
Description:	1 online resource (221 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 73-01, Section: B, page: 2400.
Contained By:	Dissertation Abstracts International73-01B.
Subject:	Bioinformatics. -
Online resource:	click for full text (PQDT)
ISBN:	9781124951089

Algorithms, Data Structures, and Statistical Analyses for Improving Chemical Database Searches.
Nasr, Ramzi J.

Algorithms, Data Structures, and Statistical Analyses for Improving Chemical Database Searches. - 1 online resource (221 pages)

Source: Dissertation Abstracts International, Volume: 73-01, Section: B, page: 2400.

Thesis (Ph.D.)

Includes bibliographical references

This item is not available from ProQuest Dissertations & Theses.

The objective of many cheminformatics applications is to effectively search large databases of molecules. For example, in ligand-based screening, the searches can identify lead molecules that are most likely to bind to a drug target, typically a protein receptor or enzyme, at the earliest stage of drug discovery. Identification of lead molecules in those early stages may decrease the cost of drug discovery and speed up the overall development time. The ongoing expansion of databases of molecules, as new compounds are discovered or synthesized, and the potential exploration of the large chemical space of virtual compounds, creates a need for improving search quality and efficiency. The work presented here addresses the need in three ways: (1) computing the statistical significance of similarity scores efficiently to give them biological relevance; (2) developing algorithms and data structures for speeding up the searches of very large databases by many folds; and (3) formulating and validating new similarity metrics to find biologically relevant molecules given a set of multiple query molecules.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9781124951089Subjects--Topical Terms:

583857
Bioinformatics.
Index Terms--Genre/Form:

554714
Electronic books.

Algorithms, Data Structures, and Statistical Analyses for Improving Chemical Database Searches.
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100 1 $a Nasr, Ramzi J. $3 1182072
245 1 0 $a Algorithms, Data Structures, and Statistical Analyses for Improving Chemical Database Searches.
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300 $a 1 online resource (221 pages)
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500 $a Source: Dissertation Abstracts International, Volume: 73-01, Section: B, page: 2400.
500 $a Adviser: Pierre Baldi.
502 $a Thesis (Ph.D.) $c University of California, Irvine $d 2011.
504 $a Includes bibliographical references
506 $a This item is not available from ProQuest Dissertations & Theses.
520 $a The objective of many cheminformatics applications is to effectively search large databases of molecules. For example, in ligand-based screening, the searches can identify lead molecules that are most likely to bind to a drug target, typically a protein receptor or enzyme, at the earliest stage of drug discovery. Identification of lead molecules in those early stages may decrease the cost of drug discovery and speed up the overall development time. The ongoing expansion of databases of molecules, as new compounds are discovered or synthesized, and the potential exploration of the large chemical space of virtual compounds, creates a need for improving search quality and efficiency. The work presented here addresses the need in three ways: (1) computing the statistical significance of similarity scores efficiently to give them biological relevance; (2) developing algorithms and data structures for speeding up the searches of very large databases by many folds; and (3) formulating and validating new similarity metrics to find biologically relevant molecules given a set of multiple query molecules.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Bioinformatics. $3 583857
650 4 $a Computer science. $3 573171
650 4 $a Statistics. $3 556824
655 7 $a Electronic books. $2 local $3 554714
690 $a 0715
690 $a 0984
690 $a 0463
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a University of California, Irvine. $b Computer Science - Ph.D.. $3 1182073
773 0 $t Dissertation Abstracts International $g 73-01B.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3477971 $z click for full text (PQDT)