Predicting biodegradation products and pathways: a hybrid knowledge- and machine learning-based approach

MOTIVATION Current methods for the prediction of biodegradation products and pathways of organic environmental pollutants either do not take into account domain knowledge or do not provide probability estimates. In this article, we propose a hybrid knowledge- and machine learning-based approach to overcome these limitations in the context of the University of Minnesota Pathway Prediction System (UM-PPS). The proposed solution performs relative reasoning in a machine learning framework, and obtains one probability estimate for each biotransformation rule of the system. As the application of a rule then depends on a threshold for the probability estimate, the trade-off between recall (sensitivity) and precision (selectivity) can be addressed and leveraged in practice. RESULTS Results from leave-one-out cross-validation show that a recall and precision of approximately 0.8 can be achieved for a subset of 13 transformation rules. Therefore, it is possible to optimize precision without compromising recall. We are currently integrating the results into an experimental version of the UM-PPS server. AVAILABILITY The program is freely available on the web at http://wwwkramer.in.tum.de/research/applications/biodegradation/data. CONTACT kramer@in.tum.de.

• Publication year

  2010

• Venue

  Bioinform.

• Publication date

  2010-01-26

• Fields of study

  Medicine, Computer Science, Environmental Science

• Identifiers
  DOI 10.1093/bioinformatics/btq024PMID 20106820
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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