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

BMaD

Boolean matrix decomposition is a method to obtain a compressed representation of a matrix with Boolean entries. BMaD (Boolean Matrix Decomposition Framework) is a modular framework, written in Java, that unifies several Boolean matrix decomposition algorithms, and provide methods to evaluate their performance. The main advantages of the framework are its modular approach and hence the flexible combination of the steps of a Boolean matrix decomposition and the capability of handling missing values.

BMaD is available at GitHub. MLC-BMaD, a multi-label classifier using Boolean matrix decomposition (implemented using the BMaD library) is also available at GitHub.

2014

Tyukin, Andrey; Kramer, Stefan; Wicker, Jörg

BMaD - A Boolean Matrix Decomposition Framework Inproceedings

Calders, Toon; Esposito, Floriana; Hüllermeier, Eyke; Meo, Rosa (Ed.): Machine Learning and Knowledge Discovery in Databases, pp. 481-484, Springer Berlin Heidelberg, 2014, ISBN: 978-3-662-44844-1.

Abstract | Links | BibTeX | Altmetric

2012

Wicker, Jörg; Pfahringer, Bernhard; Kramer, Stefan

Multi-label Classification Using Boolean Matrix Decomposition Inproceedings

Proceedings of the 27th Annual ACM Symposium on Applied Computing, pp. 179–186, ACM, Trento, Italy, 2012, ISBN: 978-1-4503-0857-1.

Abstract | Links | BibTeX | Altmetric

OpenTox

The overall objective of the EU FP7 project OpenTox was to develop a framework that provides a unified access to toxicity data, (Q)SAR models, procedures supporting validation and additional information that helps with the interpretation of (Q)SAR predictions. The OpenTox framework has been developed as an open source project to optimize the dissemination and impact, to allow the inspection and review of algorithms and to attract external contributors. We closely collaborated with related projects (e.g. OECD toolbox) and authorities to agree on common standards and to avoid duplicated and redundant work. The project was very international with partners from Switzerland, Bulgaria, Italy, Greece, Russia, India, USA and Germany. The  partners were from academic, government and economic background. Additionally, the project had an advisory board with members from government organizations and industry. I was researcher, algorithm and REST web service developer in this project.

2013

Wicker, Jörg

Large Classifier Systems in Bio- and Cheminformatics PhD Thesis

Technische Universität München, 2013.

Abstract | Links | BibTeX

2010

Hardy, Barry; Douglas, Nicki; Helma, Christoph; Rautenberg, Micha; Jeliazkova, Nina; Jeliazkov, Vedrin; Nikolova, Ivelina; Benigni, Romualdo; Tcheremenskaia, Olga; Kramer, Stefan; Girschick, Tobias; Buchwald, Fabian; Wicker, Jörg; Karwath, Andreas; Gütlein, Martin; Maunz, Andreas; Sarimveis, Haralambos; Melagraki, Georgia; Afantitis, Antreas; Sopasakis, Pantelis; Gallagher, David; Poroikov, Vladimir; Filimonov, Dmitry; Zakharov, Alexey; Lagunin, Alexey; Gloriozova, Tatyana; Novikov, Sergey; Skvortsova, Natalia; Druzhilovsky, Dmitry; Chawla, Sunil; Ghosh, Indira; Ray, Surajit; Patel, Hitesh; Escher, Sylvia

Collaborative development of predictive toxicology applications Journal Article

Journal of Cheminformatics, 2 (1), pp. 7, 2010, ISSN: 1758-2946.

Abstract | Links | BibTeX | Altmetric

A Nonlinear Label Compression and Transformation Method for Multi-Label Classification using Autoencoders

Jörg Wicker, Andrey Tyukin, Stefan Kramer (2016): A Nonlinear Label Compression and Transformation Method for Multi-Label Classification using Autoencoders. In: 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. 328-340, Springer International Publishing, Switzerland, 2016, ISBN: 978-3-319-31753-3.

Read more

BMaD – A Boolean Matrix Decomposition Framework

Andrey Tyukin, Stefan Kramer, Jörg Wicker (2014): BMaD - A Boolean Matrix Decomposition Framework. In: Calders, Toon; Esposito, Floriana; Hüllermeier, Eyke; Meo, Rosa (Ed.): Machine Learning and Knowledge Discovery in Databases, pp. 481-484, Springer Berlin Heidelberg, 2014, ISBN: 978-3-662-44844-1.

Read more