enviPath

enviPath is both, a database and a prediction system, for the microbial biotransformation of organic environmental contaminants. The database provides the possibility to store and view experimentally observed biotransformation pathways, and supports the annotation of pathways with experimental and environmental conditions. The pathway prediction system provides different relative reasoning models to predict likely biotransformation pathways and products.

Database

The enviPath database stores reviewed pathways from the scientific literature and predicted or user-entered pathways. The default package currently consists mainly of the pathways from the former EAWAG-BBD system. You can browse using the top menu. The database is organized in packages. Each package has an owner who can grant reading or writing permissions. We list data only as reviewed if it is reviewed by one of the organisations or groups in the reviewer group.

Prediction

enviPath can be used to predict biotransformation pathways. You can do this by simply using the input field on the start page. Enter a compound in SMILES format, or draw it using the molecule editor (by clicking on the dropdown on the left), and click on “Go!”. If the pathway for this compound was predicted before and is found in the database, a list of corresponding pathways will be returned, otherwise, the system will predict the pathway. Note that for anonymous users there is a limit to computation time and size of the predicted pathways. The resulting pathway will be stored in the database for 30 days and will be accessible and changeable for everyone. If you want to store the pathway for longer, prevent others from changing or seeing your pathways, or use more resources in terms of computation time and size of pathways, create an account (using the login button above) and set appropriate permissions for your data packages (the default settings should be suitable for most users).

enviPath is available at https://envipath.org.

Slides about enviPath are available here.

2017

Latino, Diogo; Wicker, Jörg; Gütlein, Martin; Schmid, Emanuel; Kramer, Stefan; Fenner, Kathrin

Eawag-Soil in enviPath: a new resource for exploring regulatory pesticide soil biodegradation pathways and half-life data Journal Article

Environmental Science: Process & Impact, 2017.

Abstract | Links | BibTeX | Altmetric

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. 75-97, Springer International Publishing, Cham, 2016, ISBN: 978-3-319-31858-5.

Abstract | Links | BibTeX | Altmetric

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. D502-D508, 2016.

Abstract | Links | BibTeX | Altmetric

2013

Wicker, Jörg

Large Classifier Systems in Bio- and Cheminformatics PhD Thesis

Technische Universität München, 2013.

Abstract | Links | BibTeX

2010

Wicker, Jörg; Fenner, Kathrin; Ellis, Lynda; Wackett, Larry; Kramer, Stefan

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

Bioinformatics, 26 (6), pp. 814-821, 2010.

Abstract | Links | BibTeX | Altmetric

2008

Wicker, Jörg; Fenner, Kathrin; Ellis, Lynda; Wackett, Larry; Kramer, Stefan

Machine Learning and Data Mining Approaches to Biodegradation Pathway Prediction Inproceedings

Bridewell, Will; Calders, Toon; de Medeiros, Ana Karla; Kramer, Stefan; Pechenizkiy, Mykola; Todorovski, Ljupco (Ed.): Proceedings of the Second International Workshop on the Induction of Process Models at ECML PKDD 2008, 2008.

Links | BibTeX

Machine Learning and Data Mining Approaches to Biodegradation Pathway Prediction

Jörg Wicker, Kathrin Fenner, Lynda Ellis, Larry Wackett, Stefan Kramer: Machine Learning and Data Mining Approaches to Biodegradation Pathway Prediction. In: Bridewell, Will; Calders, Toon; de Medeiros, Ana Karla; Kramer, Stefan; Pechenizkiy, Mykola; Todorovski, Ljupco (Ed.): Proceedings of the Second International Workshop on the Induction of Process Models at ECML PKDD 2008, 2008.

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