Multilabel classification targets the prediction of multiple interdependent and nonexclusive binary target variables. Transformationbased algorithms transform the data set such that regular singlelabel algorithms can be applied to the problem. A special type of transformationbased classifiers are label compression methods, that compress the labels and then mostly use single label classifiers to predict the compressed labels.
2017 

Latino, Diogo; Wicker, Jörg; Gütlein, Martin; Schmid, Emanuel; Kramer, Stefan; Fenner, Kathrin EawagSoil in enviPath: a new resource for exploring regulatory pesticide soil biodegradation pathways and halflife data Journal Article Environmental Science: Process & Impact, 2017. Abstract  Links  BibTeX  Altmetric @article{latino2017eawag, title = {EawagSoil in enviPath: a new resource for exploring regulatory pesticide soil biodegradation pathways and halflife data}, author = {Diogo Latino and Jörg Wicker and Martin Gütlein and Emanuel Schmid and Stefan Kramer and Kathrin Fenner}, doi = {10.1039/C6EM00697C}, year = {2017}, date = {20170101}, journal = {Environmental Science: Process & Impact}, publisher = {The Royal Society of Chemistry}, abstract = {Developing models for the prediction of microbial biotransformation pathways and halflives of trace organic contaminants in different environments requires as training data easily accessible and sufficiently large collections of respective biotransformation data that are annotated with metadata on study conditions. Here, we present the EawagSoil package, a public database that has been developed to contain all freely accessible regulatory data on pesticide degradation in laboratory soil simulation studies for pesticides registered in the EU (282 degradation pathways, 1535 reactions, 1619 compounds and 4716 biotransformation halflife values with corresponding metadata on study conditions). We provide a thorough description of this novel data resource, and discuss important features of the pesticide soil degradation data that are relevant for model development. Most notably, the variability of halflife values for individual compounds is large and only about one order of magnitude lower than the entire range of median halflife values spanned by all compounds, demonstrating the need to consider study conditions in the development of more accurate models for biotransformation prediction. We further show how the data can be used to find missing rules relevant for predicting soil biotransformation pathways. From this analysis, eight examples of reaction types were presented that should trigger the formulation of new biotransformation rules, e.g., ArOH methylation, or the extension of existing rules e.g., hydroxylation in aliphatic rings. The data were also used to exemplarily explore the dependence of halflives of different amide pesticides on chemical class and experimental parameters. This analysis highlighted the value of considering initial transformation reactions for the development of meaningful quantitativestructure biotransformation relationships (QSBR), which is a novel opportunity of f ered by the simultaneous encoding of transformation reactions and corresponding halflives in EawagSoil. Overall, EawagSoil provides an unprecedentedly rich collection of manually extracted and curated biotransformation data, which should be useful in a great variety of applications.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Developing models for the prediction of microbial biotransformation pathways and halflives of trace organic contaminants in different environments requires as training data easily accessible and sufficiently large collections of respective biotransformation data that are annotated with metadata on study conditions. Here, we present the EawagSoil package, a public database that has been developed to contain all freely accessible regulatory data on pesticide degradation in laboratory soil simulation studies for pesticides registered in the EU (282 degradation pathways, 1535 reactions, 1619 compounds and 4716 biotransformation halflife values with corresponding metadata on study conditions). We provide a thorough description of this novel data resource, and discuss important features of the pesticide soil degradation data that are relevant for model development. Most notably, the variability of halflife values for individual compounds is large and only about one order of magnitude lower than the entire range of median halflife values spanned by all compounds, demonstrating the need to consider study conditions in the development of more accurate models for biotransformation prediction. We further show how the data can be used to find missing rules relevant for predicting soil biotransformation pathways. From this analysis, eight examples of reaction types were presented that should trigger the formulation of new biotransformation rules, e.g., ArOH methylation, or the extension of existing rules e.g., hydroxylation in aliphatic rings. The data were also used to exemplarily explore the dependence of halflives of different amide pesticides on chemical class and experimental parameters. This analysis highlighted the value of considering initial transformation reactions for the development of meaningful quantitativestructure biotransformation relationships (QSBR), which is a novel opportunity of f ered by the simultaneous encoding of transformation reactions and corresponding halflives in EawagSoil. Overall, EawagSoil provides an unprecedentedly rich collection of manually extracted and curated biotransformation data, which should be useful in a great variety of applications.  
2016 

Wicker, Jörg; Fenner, Kathrin; Kramer, Stefan A Hybrid Machine Learning and Knowledge Based Approach to Limit Combinatorial Explosion in Biodegradation Prediction Incollection Lässig, Jörg; Kersting, Kristian; Morik, Katharina (Ed.): Computational Sustainability, pp. 7597, Springer International Publishing, Cham, 2016, ISBN: 9783319318585. Abstract  Links  BibTeX  Altmetric @incollection{wicker2016ahybrid, title = {A Hybrid Machine Learning and Knowledge Based Approach to Limit Combinatorial Explosion in Biodegradation Prediction}, author = {Jörg Wicker and Kathrin Fenner and Stefan Kramer}, editor = {Jörg Lässig and Kristian Kersting and Katharina Morik}, url = {http://dx.doi.org/10.1007/9783319318585_5}, doi = {10.1007/9783319318585_5}, isbn = {9783319318585}, year = {2016}, date = {20160421}, booktitle = {Computational Sustainability}, pages = {7597}, publisher = {Springer International Publishing}, address = {Cham}, 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 knowledgebased and machine learningbased approach to overcome these limitations in the context of the University of Minnesota Pathway Prediction System (UMPPS). 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 tradeoff between recall (sensitivity) and precision (selectivity) can be addressed and leveraged in practice. Results from leaveoneout crossvalidation 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 multilabel 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 multilabel classification. Therefore, it is possible to optimize precision without compromising recall. Recently, we integrated the presented approach into enviPath, a complete redesign and reimplementation of UMPPS.}, keywords = {}, pubstate = {published}, tppubtype = {incollection} } 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 knowledgebased and machine learningbased approach to overcome these limitations in the context of the University of Minnesota Pathway Prediction System (UMPPS). 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 tradeoff between recall (sensitivity) and precision (selectivity) can be addressed and leveraged in practice. Results from leaveoneout crossvalidation 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 multilabel 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 multilabel classification. Therefore, it is possible to optimize precision without compromising recall. Recently, we integrated the presented approach into enviPath, a complete redesign and reimplementation of UMPPS.  
Wicker, Jörg; Tyukin, Andrey; Kramer, Stefan A Nonlinear Label Compression and Transformation Method for MultiLabel Classification using Autoencoders Inproceedings Bailey, James; Khan, Latifur; Washio, Takashi; Dobbie, Gill; Huang, Zhexue Joshua; Wang, Ruili (Ed.): The 20th Pacific Asia Conference on Knowledge Discovery and Data Mining (PAKDD), pp. 328340, Springer International Publishing, Switzerland, 2016, ISBN: 9783319317533. Abstract  Links  BibTeX  Altmetric @inproceedings{wicker2016nonlinear, title = {A Nonlinear Label Compression and Transformation Method for MultiLabel Classification using Autoencoders}, author = {Jörg Wicker and Andrey Tyukin and Stefan Kramer}, editor = {James Bailey and Latifur Khan and Takashi Washio and Gill Dobbie and Zhexue Joshua Huang and Ruili Wang}, url = {http://dx.doi.org/10.1007/9783319317533_27}, doi = {10.1007/9783319317533_27}, isbn = {9783319317533}, year = {2016}, date = {20160416}, booktitle = {The 20th Pacific Asia Conference on Knowledge Discovery and Data Mining (PAKDD)}, volume = {9651}, pages = {328340}, publisher = {Springer International Publishing}, address = {Switzerland}, series = {Lecture Notes in Computer Science}, abstract = {Multilabel classification targets the prediction of multiple interdependent and nonexclusive binary target variables. Transformationbased algorithms transform the data set such that regular singlelabel algorithms can be applied to the problem. A special type of transformationbased classifiers are label compression methods, that compress the labels and then mostly use single label classifiers to predict the compressed labels. So far, there are no compressionbased algorithms follow a problem transformation approach and address nonlinear dependencies in the labels. In this paper, we propose a new algorithm, called Maniac (MultilAbel classificatioN usIng AutoenCoders), which extracts the nonlinear dependencies by compressing the labels using autoencoders. We adapt the training process of autoencoders in a way to make them more suitable for a parameter optimization in the context of this algorithm. The method is evaluated on eight standard multilabel data sets. Experiments show that despite not producing a good ranking, Maniac generates a particularly good bipartition of the labels into positives and negatives. This is caused by rather strong predictions with either really high or low probability. Additionally, the algorithm seems to perform better given more labels and a higher label cardinality in the data set.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Multilabel classification targets the prediction of multiple interdependent and nonexclusive binary target variables. Transformationbased algorithms transform the data set such that regular singlelabel algorithms can be applied to the problem. A special type of transformationbased classifiers are label compression methods, that compress the labels and then mostly use single label classifiers to predict the compressed labels. So far, there are no compressionbased algorithms follow a problem transformation approach and address nonlinear dependencies in the labels. In this paper, we propose a new algorithm, called Maniac (MultilAbel classificatioN usIng AutoenCoders), which extracts the nonlinear dependencies by compressing the labels using autoencoders. We adapt the training process of autoencoders in a way to make them more suitable for a parameter optimization in the context of this algorithm. The method is evaluated on eight standard multilabel data sets. Experiments show that despite not producing a good ranking, Maniac generates a particularly good bipartition of the labels into positives and negatives. This is caused by rather strong predictions with either really high or low probability. Additionally, the algorithm seems to perform better given more labels and a higher label cardinality in the data set.  
Wicker, Jörg; Lorsbach, Tim; Gütlein, Martin; Schmid, Emanuel; Latino, Diogo; Kramer, Stefan; Fenner, Kathrin enviPath  The Environmental Contaminant Biotransformation Pathway Resource Journal Article Nucleic Acid Research, 44 (D1), pp. D502D508, 2016. Abstract  Links  BibTeX  Altmetric @article{wicker2016envipath, title = {enviPath  The Environmental Contaminant Biotransformation Pathway Resource}, author = {Jörg Wicker and Tim Lorsbach and Martin Gütlein and Emanuel Schmid and Diogo Latino and Stefan Kramer and Kathrin Fenner}, editor = {Michael Galperin}, url = {http://nar.oxfordjournals.org/content/44/D1/D502.abstract}, doi = {10.1093/nar/gkv1229}, year = {2016}, date = {20160101}, journal = {Nucleic Acid Research}, volume = {44}, number = {D1}, pages = {D502D508}, abstract = {The University of Minnesota Biocatalysis/Biodegradation Database and Pathway Prediction System (UMBBD/PPS) has been a unique resource covering microbial biotransformation pathways of primarily xenobiotic chemicals for over 15 years. This paper introduces the successor system, enviPath (The Environmental Contaminant Biotransformation Pathway Resource), which is a complete redesign and reimplementation of UMBBD/PPS. enviPath uses the database from the UMBBD/PPS as a basis, extends the use of this database, and allows users to include their own data to support multiple use cases. Relative reasoning is supported for the refinement of predictions and to allow its extensions in terms of previously published, but not implemented machine learning models. User access is simplified by providing a REST API that simplifies the inclusion of enviPath into existing workflows. An RDF database is used to enable simple integration with other databases. enviPath is publicly available at https://envipath.org with free and open access to its core data.}, keywords = {}, pubstate = {published}, tppubtype = {article} } The University of Minnesota Biocatalysis/Biodegradation Database and Pathway Prediction System (UMBBD/PPS) has been a unique resource covering microbial biotransformation pathways of primarily xenobiotic chemicals for over 15 years. This paper introduces the successor system, enviPath (The Environmental Contaminant Biotransformation Pathway Resource), which is a complete redesign and reimplementation of UMBBD/PPS. enviPath uses the database from the UMBBD/PPS as a basis, extends the use of this database, and allows users to include their own data to support multiple use cases. Relative reasoning is supported for the refinement of predictions and to allow its extensions in terms of previously published, but not implemented machine learning models. User access is simplified by providing a REST API that simplifies the inclusion of enviPath into existing workflows. An RDF database is used to enable simple integration with other databases. enviPath is publicly available at https://envipath.org with free and open access to its core data.  
2013 

Wicker, Jörg Large Classifier Systems in Bio and Cheminformatics PhD Thesis Technische Universität München, 2013. @phdthesis{wicker2013large, title = {Large Classifier Systems in Bio and Cheminformatics}, author = {Jörg Wicker}, url = {http://mediatum.ub.tum.de/node?id=1165858}, year = {2013}, date = {20130101}, school = {Technische Universität München}, abstract = {Large classifier systems are machine learning algorithms that use multiple classifiers to improve the prediction of target values in advanced classification tasks. Although learning problems in bio and cheminformatics commonly provide data in schemes suitable for large classifier systems, they are rarely used in these domains. This thesis introduces two new classifiers incorporating systems of classifiers using Boolean matrix decomposition to handle data in a schema that often occurs in bio and cheminformatics. The first approach, called MLCBMaD (multilabel classification using Boolean matrix decomposition), uses Boolean matrix decomposition to decompose the labels in a multilabel classification task. The decomposed matrices are a compact representation of the information in the labels (first matrix) and the dependencies among the labels (second matrix). The first matrix is used in a further multilabel classification while the second matrix is used to generate the final matrix from the predicted values of the first matrix. MLCBMaD was evaluated on six standard multilabel data sets, the experiments showed that MLCBMaD can perform particularly well on data sets with a high number of labels and a small number of instances and can outperform standard multilabel algorithms. Subsequently, MLCBMaD is extended to a special case of multirelational learning, by considering the labels not as simple labels, but instances. The algorithm, called ClassFact (Classification factorization), uses both matrices in a multilabel classification. Each label represents a mapping between two instances. Experiments on three data sets from the domain of bioinformatics show that ClassFact can outperform the baseline method, which merges the relations into one, on hard classification tasks. Furthermore, large classifier systems are used on two cheminformatics data sets, the first one is used to predict the environmental fate of chemicals by predicting biodegradation pathways. The second is a data set from the domain of predictive toxicology. In biodegradation pathway prediction, I extend a knowledgebased system and incorporate a machine learning approach to predict a probability for biotransformation products based on the structure and knowledgebased predictions of products, which are based on transformation rules. The use of multilabel classification improves the performance of the classifiers and extends the number of transformation rules that can be covered. For the prediction of toxic effects of chemicals, I applied large classifier systems to the ToxCasttexttrademark data set, which maps toxic effects to chemicals. As the given toxic effects are not easy to predict due to missing information and a skewed class distribution, I introduce a filtering step in the multilabel classification, which finds labels that are usable in multilabel prediction and does not take the others in the prediction into account. Experiments show that this approach can improve upon the baseline method using binary classification, as well as multilabel approaches using no filtering. The presented results show that large classifier systems can play a role in future research challenges, especially in bio and cheminformatics, where data sets frequently consist of more complex structures and data can be rather small in terms of the number of instances compared to other domains.}, keywords = {}, pubstate = {published}, tppubtype = {phdthesis} } Large classifier systems are machine learning algorithms that use multiple classifiers to improve the prediction of target values in advanced classification tasks. Although learning problems in bio and cheminformatics commonly provide data in schemes suitable for large classifier systems, they are rarely used in these domains. This thesis introduces two new classifiers incorporating systems of classifiers using Boolean matrix decomposition to handle data in a schema that often occurs in bio and cheminformatics. The first approach, called MLCBMaD (multilabel classification using Boolean matrix decomposition), uses Boolean matrix decomposition to decompose the labels in a multilabel classification task. The decomposed matrices are a compact representation of the information in the labels (first matrix) and the dependencies among the labels (second matrix). The first matrix is used in a further multilabel classification while the second matrix is used to generate the final matrix from the predicted values of the first matrix. MLCBMaD was evaluated on six standard multilabel data sets, the experiments showed that MLCBMaD can perform particularly well on data sets with a high number of labels and a small number of instances and can outperform standard multilabel algorithms. Subsequently, MLCBMaD is extended to a special case of multirelational learning, by considering the labels not as simple labels, but instances. The algorithm, called ClassFact (Classification factorization), uses both matrices in a multilabel classification. Each label represents a mapping between two instances. Experiments on three data sets from the domain of bioinformatics show that ClassFact can outperform the baseline method, which merges the relations into one, on hard classification tasks. Furthermore, large classifier systems are used on two cheminformatics data sets, the first one is used to predict the environmental fate of chemicals by predicting biodegradation pathways. The second is a data set from the domain of predictive toxicology. In biodegradation pathway prediction, I extend a knowledgebased system and incorporate a machine learning approach to predict a probability for biotransformation products based on the structure and knowledgebased predictions of products, which are based on transformation rules. The use of multilabel classification improves the performance of the classifiers and extends the number of transformation rules that can be covered. For the prediction of toxic effects of chemicals, I applied large classifier systems to the ToxCasttexttrademark data set, which maps toxic effects to chemicals. As the given toxic effects are not easy to predict due to missing information and a skewed class distribution, I introduce a filtering step in the multilabel classification, which finds labels that are usable in multilabel prediction and does not take the others in the prediction into account. Experiments show that this approach can improve upon the baseline method using binary classification, as well as multilabel approaches using no filtering. The presented results show that large classifier systems can play a role in future research challenges, especially in bio and cheminformatics, where data sets frequently consist of more complex structures and data can be rather small in terms of the number of instances compared to other domains.  
2012 

Wicker, Jörg; Pfahringer, Bernhard; Kramer, Stefan Multilabel Classification Using Boolean Matrix Decomposition Inproceedings Proceedings of the 27th Annual ACM Symposium on Applied Computing, pp. 179–186, ACM, 2012, ISBN: 9781450308571. Abstract  Links  BibTeX  Altmetric @inproceedings{wicker2012multi, title = {Multilabel Classification Using Boolean Matrix Decomposition}, author = {Jörg Wicker and Bernhard Pfahringer and Stefan Kramer}, url = {https://wicker.nz/nwpacm/authorize.php?id=N10032 http://doi.acm.org/10.1145/2245276.2245311}, doi = {10.1145/2245276.2245311}, isbn = {9781450308571}, year = {2012}, date = {20120101}, booktitle = {Proceedings of the 27th Annual ACM Symposium on Applied Computing}, pages = {179186}, publisher = {ACM}, series = {SAC '12}, abstract = {This paper introduces a new multilabel classifier based on Boolean matrix decomposition. Boolean matrix decomposition is used to extract, from the full label matrix, latent labels representing useful Boolean combinations of the original labels. Base level models predict latent labels, which are subsequently transformed into the actual labels by Boolean matrix multiplication with the second matrix from the decomposition. The new method is tested on six publicly available datasets with varying numbers of labels. The experimental evaluation shows that the new method works particularly well on datasets with a large number of labels and strong dependencies among them.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } This paper introduces a new multilabel classifier based on Boolean matrix decomposition. Boolean matrix decomposition is used to extract, from the full label matrix, latent labels representing useful Boolean combinations of the original labels. Base level models predict latent labels, which are subsequently transformed into the actual labels by Boolean matrix multiplication with the second matrix from the decomposition. The new method is tested on six publicly available datasets with varying numbers of labels. The experimental evaluation shows that the new method works particularly well on datasets with a large number of labels and strong dependencies among them. 