Abstract
One of the main tasks in chemical industry regarding the sustainability of a product is the prediction of its environmental fate, i.e., its degradation products and pathways. 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 chapter, we propose a hybrid knowledge-based 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. Since 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 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. The set of used rules is further extended using multi-label classification, where dependencies among the transformation rules are exploited to improve the predictions. While the results regarding recall and precision vary, the area under the ROC curve can be improved using multi-label classification. Therefore, it is possible to optimize precision without compromising recall. Recently, we integrated the presented approach into enviPath, a complete redesign and re-implementation of UM-PPS.
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Notes
- 1.
For an overview of multi-label classification see the paper by Tsoumakas et al. [23].
- 2.
Those 25 pesticides were also tested in our previous experiments investigating the sensitivity and selectivity of the method (see Table 6 in [7]). In addition, 22 other xenobiotics (pharmaceuticals) were only used for determining the reduction of predictions (see Table 4) because their degradation products are not known.
- 3.
We count the false negatives in a slightly different way than in a previous paper [7], as we only consider products that are suggested by any of the biotransformation rules. In other words, we do not take into account products of reactions that are not subsumed by any of the rules. This is done because only for the products suggested by the UM-PPS, the method proposed here becomes effective—the classifiers can only restrict the rules, not extend them.
- 4.
Note that any other machine learning algorithm for classification and, similarly, any other method for the computation of substructural or other molecular descriptors could be applied to the problem.
- 5.
We cannot compare our results with those of CATABOL because the system is proprietary and cannot be trained to predict the probability of individual rules—the pathway structure has to be fixed for training (for details we refer to Sect. 7). This means that CATABOL addresses a different problem than the approach presented here.
- 6.
In other words, it shows that informed classifiers do not pay for the rest of the rules.
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Wicker, J., Fenner, K., Kramer, S. (2016). A Hybrid Machine Learning and Knowledge Based Approach to Limit Combinatorial Explosion in Biodegradation Prediction. In: Lässig, J., Kersting, K., Morik, K. (eds) Computational Sustainability. Studies in Computational Intelligence, vol 645. Springer, Cham. https://doi.org/10.1007/978-3-319-31858-5_5
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