Data-driven extraction of relative reasoning rules to limit combinatorial explosion in biodegradation pathway prediction

Kathrin Fenner, Junfeng Gao, Stefan Kramer, Lynda Ellis, Larry Wackett

Research output: Contribution to journalArticlepeer-review

41 Scopus citations

Abstract

Motivation: The University of Minnesota Pathway Prediction System (UM-PPS) is a rule-based expert system to predict plausible biodegradation pathways for organic compounds. However, iterative application of these rules to generate biodegradation pathways leads to combinatorial explosion. We use data from known biotransformation pathways to rationally determine biotransformation priorities (relative reasoning rules) to limit this explosion. Results: A total of 112 relative reasoning rules were identified and implemented. In one prediction step, i.e. as per one generation predicted, the use of relative reasoning decreases the predicted biotransformations by over 25% for 50 compounds used to generate the rules and by about 15% for an external validation set of 47 xenobiotics, including pesticides, biocides and pharmaceuticals. The percentage of correctly predicted, experimentally known products remains at 75% when relative reasoning is used. The set of relative reasoning rules identified, therefore, effectively reduces the number of predicted transformation products without compromising the quality of the predictions.

Original languageEnglish (US)
Pages (from-to)2079-2085
Number of pages7
JournalBioinformatics
Volume24
Issue number18
DOIs
StatePublished - Sep 2008

Bibliographical note

Funding Information:
Funding: Fellowship was granted for advanced researchers from the Swiss National Science Foundation (PA002-113140 to K.F.), Lhasa Limited, the US National Science Foundation (NSF0543416), and the University of Minnesota Supercomputing Institute.

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