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The University of Minnesota pathway prediction system: predicting metabolic logic
The University of Minnesota pathway prediction system (UM-PPS, http://umbbd.msi.umn.edu/predict/) recognizes functional groups in organic compounds that are potential targets of microbial catabolic reactions, and predicts transformations of these groups based on biotransformation rules. Rules are ba...
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Formato: | Texto |
Lenguaje: | English |
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Oxford University Press
2008
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2447765/ https://www.ncbi.nlm.nih.gov/pubmed/18524801 http://dx.doi.org/10.1093/nar/gkn315 |
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author | Ellis, Lynda B.M. Gao, Junfeng Fenner, Kathrin Wackett, Lawrence P. |
author_facet | Ellis, Lynda B.M. Gao, Junfeng Fenner, Kathrin Wackett, Lawrence P. |
author_sort | Ellis, Lynda B.M. |
collection | PubMed |
description | The University of Minnesota pathway prediction system (UM-PPS, http://umbbd.msi.umn.edu/predict/) recognizes functional groups in organic compounds that are potential targets of microbial catabolic reactions, and predicts transformations of these groups based on biotransformation rules. Rules are based on the University of Minnesota biocatalysis/biodegradation database (http://umbbd.msi.umn.edu/) and the scientific literature. As rules were added to the UM-PPS, more of them were triggered at each prediction step. The resulting combinatorial explosion is being addressed in four ways. Biodegradation experts give each rule an aerobic likelihood value of Very Likely, Likely, Neutral, Unlikely or Very Unlikely. Users now can choose whether they view all, or only the more aerobically likely, predicted transformations. Relative reasoning, allowing triggering of some rules to inhibit triggering of others, was implemented. Rules were initially assigned to individual chemical reactions. In selected cases, these have been replaced by super rules, which include two or more contiguous reactions that form a small pathway of their own. Rules are continually modified to improve the prediction accuracy; increasing rule stringency can improve predictions and reduce extraneous choices. The UM-PPS is freely available to all without registration. Its value to the scientific community, for academic, industrial and government use, is good and will only increase. |
format | Text |
id | pubmed-2447765 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2008 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-24477652008-07-09 The University of Minnesota pathway prediction system: predicting metabolic logic Ellis, Lynda B.M. Gao, Junfeng Fenner, Kathrin Wackett, Lawrence P. Nucleic Acids Res Articles The University of Minnesota pathway prediction system (UM-PPS, http://umbbd.msi.umn.edu/predict/) recognizes functional groups in organic compounds that are potential targets of microbial catabolic reactions, and predicts transformations of these groups based on biotransformation rules. Rules are based on the University of Minnesota biocatalysis/biodegradation database (http://umbbd.msi.umn.edu/) and the scientific literature. As rules were added to the UM-PPS, more of them were triggered at each prediction step. The resulting combinatorial explosion is being addressed in four ways. Biodegradation experts give each rule an aerobic likelihood value of Very Likely, Likely, Neutral, Unlikely or Very Unlikely. Users now can choose whether they view all, or only the more aerobically likely, predicted transformations. Relative reasoning, allowing triggering of some rules to inhibit triggering of others, was implemented. Rules were initially assigned to individual chemical reactions. In selected cases, these have been replaced by super rules, which include two or more contiguous reactions that form a small pathway of their own. Rules are continually modified to improve the prediction accuracy; increasing rule stringency can improve predictions and reduce extraneous choices. The UM-PPS is freely available to all without registration. Its value to the scientific community, for academic, industrial and government use, is good and will only increase. Oxford University Press 2008-07-01 2008-06-04 /pmc/articles/PMC2447765/ /pubmed/18524801 http://dx.doi.org/10.1093/nar/gkn315 Text en © 2008 The Author(s) http://creativecommons.org/licenses/by-nc/2.0/uk/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Articles Ellis, Lynda B.M. Gao, Junfeng Fenner, Kathrin Wackett, Lawrence P. The University of Minnesota pathway prediction system: predicting metabolic logic |
title | The University of Minnesota pathway prediction system: predicting metabolic logic |
title_full | The University of Minnesota pathway prediction system: predicting metabolic logic |
title_fullStr | The University of Minnesota pathway prediction system: predicting metabolic logic |
title_full_unstemmed | The University of Minnesota pathway prediction system: predicting metabolic logic |
title_short | The University of Minnesota pathway prediction system: predicting metabolic logic |
title_sort | university of minnesota pathway prediction system: predicting metabolic logic |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2447765/ https://www.ncbi.nlm.nih.gov/pubmed/18524801 http://dx.doi.org/10.1093/nar/gkn315 |
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