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Improved prediction of biodegradatio...

Gao, Junfeng.

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Improved prediction of biodegradation pathways: Visualization and performance.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Improved prediction of biodegradation pathways: Visualization and performance./
Author:	Gao, Junfeng.
Description:	102 p.
Notes:	Source: Dissertation Abstracts International, Volume: 72-06, Section: B, page: .
Contained By:	Dissertation Abstracts International72-06B.
Subject:	Biology, Bioinformatics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3445410
ISBN:	9781124549781

Improved prediction of biodegradation pathways: Visualization and performance.
Gao, Junfeng.

Improved prediction of biodegradation pathways: Visualization and performance. - 102 p.

Source: Dissertation Abstracts International, Volume: 72-06, Section: B, page: .

Thesis (Ph.D.)--University of Minnesota, 2011.

The University of Minnesota Pathway Prediction System (UM-PPS) (http://umbbd.msi.umn.edu/predict/) is a rule-based system that predicts plausible pathways for microbial degradation of organic compounds. Its biotransformation rules are based on reactions found in the University of Minnesota Biocatalysis/Biodegradation Database (UM-BBD, http://umbbd.msi.umn.edu/) or in the scientific literature. Since the UM-PPS was created in 2002, its rule base has grown to 275 entries. The original system predicted one level of prediction at a time. It provided a limited view of prediction results and heavily relied on manual interventions. It matched the query compound with all biotransformation rules one by one, which was a time-consuming process.

ISBN: 9781124549781Subjects--Topical Terms:

1018415
Biology, Bioinformatics.

Improved prediction of biodegradation pathways: Visualization and performance.
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100 1 $a Gao, Junfeng. $3 1682263
245 1 0 $a Improved prediction of biodegradation pathways: Visualization and performance.
300 $a 102 p.
500 $a Source: Dissertation Abstracts International, Volume: 72-06, Section: B, page: .
500 $a Adviser: Lynda Ellis.
502 $a Thesis (Ph.D.)--University of Minnesota, 2011.
520 $a The University of Minnesota Pathway Prediction System (UM-PPS) (http://umbbd.msi.umn.edu/predict/) is a rule-based system that predicts plausible pathways for microbial degradation of organic compounds. Its biotransformation rules are based on reactions found in the University of Minnesota Biocatalysis/Biodegradation Database (UM-BBD, http://umbbd.msi.umn.edu/) or in the scientific literature. Since the UM-PPS was created in 2002, its rule base has grown to 275 entries. The original system predicted one level of prediction at a time. It provided a limited view of prediction results and heavily relied on manual interventions. It matched the query compound with all biotransformation rules one by one, which was a time-consuming process.
520 $a In 2008, the two-level visualization was first implemented to allow users to view two levels of predictions at a time. However, this visualization approach was usually not able to show the complete metabolism of a query compound, and users still needed expert knowledge to make educated choices to continue the prediction. In 2009, we started to develop a multi-level visualization and, simultaneously, work on increasing prediction speed. In 2010, the multi-level visualization was implemented to predict up to six levels of predictions at a time. Not only more products, but also common intermediates and cleavage products are displayed. Users can view prediction alternatives much more easily in a tree-like interactive graph. A multi-level prediction can be computationally intensive and requires users to wait longer than desired for the prediction results. Therefore, we used a multi-thread computing strategy that decreased the prediction run-time by half. We balanced the computing threads and pre-loaded all UM-PPS database tables to permit quick access to its data. Both of these improvements resulted in an additional 30% decrease in prediction run-time. We conducted a simulation study and used another web server to reduce the queuing interference by over 85%. Beta testers were satisfied with its visualization and performance.
520 $a The above improvements lead to a smarter and faster UM-PPS that has continued its growth in the past 4 years. It now displays better graphical results and predicts biodegradation pathway in a timely manner.
590 $a School code: 0130.
650 4 $a Biology, Bioinformatics. $3 1018415
690 $a 0715
710 2 $a University of Minnesota. $b Health Informatics. $3 1266007
773 0 $t Dissertation Abstracts International $g 72-06B.
790 1 0 $a Ellis, Lynda, $e advisor
790 1 0 $a Wackett, Lawrence $e committee member
790 1 0 $a Adam, Terrence $e committee member
790 1 0 $a Karypis, George $e committee member
790 $a 0130
791 $a Ph.D.
792 $a 2011
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3445410