2010
8.
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
In: Journal of Cheminformatics, vol. 2, no. 1, pp. 7, 2010, ISSN: 1758-2946.
Abstract | Links | BibTeX | Altmetric | PlumX | Tags: cheminformatics, computational sustainability, data mining, machine learning, REST, toxicity
@article{hardy2010collaborative,
title = {Collaborative development of predictive toxicology applications},
author = {Barry Hardy and Nicki Douglas and Christoph Helma and Micha Rautenberg and Nina Jeliazkova and Vedrin Jeliazkov and Ivelina Nikolova and Romualdo Benigni and Olga Tcheremenskaia and Stefan Kramer and Tobias Girschick and Fabian Buchwald and J\"{o}rg Wicker and Andreas Karwath and Martin G\"{u}tlein and Andreas Maunz and Haralambos Sarimveis and Georgia Melagraki and Antreas Afantitis and Pantelis Sopasakis and David Gallagher and Vladimir Poroikov and Dmitry Filimonov and Alexey Zakharov and Alexey Lagunin and Tatyana Gloriozova and Sergey Novikov and Natalia Skvortsova and Dmitry Druzhilovsky and Sunil Chawla and Indira Ghosh and Surajit Ray and Hitesh Patel and Sylvia Escher},
url = {http://www.jcheminf.com/content/2/1/7},
doi = {10.1186/1758-2946-2-7},
issn = {1758-2946},
year = {2010},
date = {2010-01-01},
journal = {Journal of Cheminformatics},
volume = {2},
number = {1},
pages = {7},
abstract = {OpenTox provides an interoperable, standards-based Framework for the support of predictive toxicology data management, algorithms, modelling, validation and reporting. It is relevant to satisfying the chemical safety assessment requirements of the REACH legislation as it supports access to experimental data, (Quantitative) Structure-Activity Relationship models, and toxicological information through an integrating platform that adheres to regulatory requirements and OECD validation principles. Initial research defined the essential components of the Framework including the approach to data access, schema and management, use of controlled vocabularies and ontologies, architecture, web service and communications protocols, and selection and integration of algorithms for predictive modelling. OpenTox provides end-user oriented tools to non-computational specialists, risk assessors, and toxicological experts in addition to Application Programming Interfaces (APIs) for developers of new applications. OpenTox actively supports public standards for data representation, interfaces, vocabularies and ontologies, Open Source approaches to core platform components, and community-based collaboration approaches, so as to progress system interoperability goals.The OpenTox Framework includes APIs and services for compounds, datasets, features, algorithms, models, ontologies, tasks, validation, and reporting which may be combined into multiple applications satisfying a variety of different user needs. OpenTox applications are based on a set of distributed, interoperable OpenTox API-compliant REST web services. The OpenTox approach to ontology allows for efficient mapping of complementary data coming from different datasets into a unifying structure having a shared terminology and representation.Two initial OpenTox applications are presented as an illustration of the potential impact of OpenTox for high-quality and consistent structure-activity relationship modelling of REACH-relevant endpoints: ToxPredict which predicts and reports on toxicities for endpoints for an input chemical structure, and ToxCreate which builds and validates a predictive toxicity model based on an input toxicology dataset. Because of the extensible nature of the standardised Framework design, barriers of interoperability between applications and content are removed, as the user may combine data, models and validation from multiple sources in a dependable and time-effective way.},
keywords = {cheminformatics, computational sustainability, data mining, machine learning, REST, toxicity},
pubstate = {published},
tppubtype = {article}
}
OpenTox provides an interoperable, standards-based Framework for the support of predictive toxicology data management, algorithms, modelling, validation and reporting. It is relevant to satisfying the chemical safety assessment requirements of the REACH legislation as it supports access to experimental data, (Quantitative) Structure-Activity Relationship models, and toxicological information through an integrating platform that adheres to regulatory requirements and OECD validation principles. Initial research defined the essential components of the Framework including the approach to data access, schema and management, use of controlled vocabularies and ontologies, architecture, web service and communications protocols, and selection and integration of algorithms for predictive modelling. OpenTox provides end-user oriented tools to non-computational specialists, risk assessors, and toxicological experts in addition to Application Programming Interfaces (APIs) for developers of new applications. OpenTox actively supports public standards for data representation, interfaces, vocabularies and ontologies, Open Source approaches to core platform components, and community-based collaboration approaches, so as to progress system interoperability goals.The OpenTox Framework includes APIs and services for compounds, datasets, features, algorithms, models, ontologies, tasks, validation, and reporting which may be combined into multiple applications satisfying a variety of different user needs. OpenTox applications are based on a set of distributed, interoperable OpenTox API-compliant REST web services. The OpenTox approach to ontology allows for efficient mapping of complementary data coming from different datasets into a unifying structure having a shared terminology and representation.Two initial OpenTox applications are presented as an illustration of the potential impact of OpenTox for high-quality and consistent structure-activity relationship modelling of REACH-relevant endpoints: ToxPredict which predicts and reports on toxicities for endpoints for an input chemical structure, and ToxCreate which builds and validates a predictive toxicity model based on an input toxicology dataset. Because of the extensible nature of the standardised Framework design, barriers of interoperability between applications and content are removed, as the user may combine data, models and validation from multiple sources in a dependable and time-effective way.
7.
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
In: Bioinformatics, vol. 26, no. 6, pp. 814-821, 2010.
Abstract | Links | BibTeX | Altmetric | PlumX | Tags: biodegradation, cheminformatics, computational sustainability, enviPath, machine learning, metabolic pathways
@article{wicker2010predicting,
title = {Predicting biodegradation products and pathways: a hybrid knowledge- and machine learning-based approach},
author = {J\"{o}rg Wicker and Kathrin Fenner and Lynda Ellis and Larry Wackett and Stefan Kramer},
url = {http://bioinformatics.oxfordjournals.org/content/26/6/814.full},
doi = {10.1093/bioinformatics/btq024},
year = {2010},
date = {2010-01-01},
journal = {Bioinformatics},
volume = {26},
number = {6},
pages = {814-821},
publisher = {Oxford University Press},
abstract = {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 ∼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},
keywords = {biodegradation, cheminformatics, computational sustainability, enviPath, machine learning, metabolic pathways},
pubstate = {published},
tppubtype = {article}
}
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 ∼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
6.
Wicker, Jörg; Richter, Lothar; Kramer, Stefan
SINDBAD and SiQL: Overview, Applications and Future Developments Book Section
In: Džeroski, Sašo; Goethals, Bart; Panov, Panče (Ed.): Inductive Databases and Constraint-Based Data Mining, pp. 289-309, Springer New York, 2010, ISBN: 978-1-4419-7737-3.
Abstract | Links | BibTeX | Altmetric | PlumX | Tags: data mining, inductive databases, machine learning, query languages
@incollection{wicker2010sindbad,
title = {SINDBAD and SiQL: Overview, Applications and Future Developments},
author = {J\"{o}rg Wicker and Lothar Richter and Stefan Kramer},
editor = {Sa\v{s}o D\v{z}eroski and Bart Goethals and Pan\v{c}e Panov},
url = {http://dx.doi.org/10.1007/978-1-4419-7738-0_12},
doi = {10.1007/978-1-4419-7738-0_12},
isbn = {978-1-4419-7737-3},
year = {2010},
date = {2010-01-01},
booktitle = {Inductive Databases and Constraint-Based Data Mining},
pages = {289-309},
publisher = {Springer New York},
abstract = {The chapter gives an overview of the current state of the Sindbad system and planned extensions. Following an introduction to the system and its query language SiQL, we present application scenarios from the areas of gene expression/regulation and small molecules. Next, we describe a web service interface to Sindbad that enables new possibilities for inductive databases (distributing tasks over multiple servers, language and platform independence, …). Finally, we discuss future plans for the system, in particular, to make the system more ‘declarative’ by the use of signatures, to integrate the useful concept of mining views into the system, and to support specific pattern domains like graphs and strings.},
keywords = {data mining, inductive databases, machine learning, query languages},
pubstate = {published},
tppubtype = {incollection}
}
The chapter gives an overview of the current state of the Sindbad system and planned extensions. Following an introduction to the system and its query language SiQL, we present application scenarios from the areas of gene expression/regulation and small molecules. Next, we describe a web service interface to Sindbad that enables new possibilities for inductive databases (distributing tasks over multiple servers, language and platform independence, …). Finally, we discuss future plans for the system, in particular, to make the system more ‘declarative’ by the use of signatures, to integrate the useful concept of mining views into the system, and to support specific pattern domains like graphs and strings.
2008
5.
Wicker, Jörg; Richter, Lothar; Kessler, Kristina; Kramer, Stefan
SINDBAD and SiQL: An Inductive Database and Query Language in the Relational Model Proceedings Article
In: Daelemans, Walter; Goethals, Bart; Morik, Katharina (Ed.): Machine Learning and Knowledge Discovery in Databases, pp. 690-694, Springer Berlin Heidelberg, 2008, ISBN: 978-3-540-87480-5.
Abstract | Links | BibTeX | Altmetric | PlumX | Tags: data mining, inductive databases, machine learning, query languages
@inproceedings{wicker2008sindbad,
title = {SINDBAD and SiQL: An Inductive Database and Query Language in the Relational Model},
author = {J\"{o}rg Wicker and Lothar Richter and Kristina Kessler and Stefan Kramer},
editor = {Walter Daelemans and Bart Goethals and Katharina Morik},
url = {http://dx.doi.org/10.1007/978-3-540-87481-2_48},
doi = {10.1007/978-3-540-87481-2_48},
isbn = {978-3-540-87480-5},
year = {2008},
date = {2008-01-01},
booktitle = {Machine Learning and Knowledge Discovery in Databases},
volume = {5212},
pages = {690-694},
publisher = {Springer Berlin Heidelberg},
series = {Lecture Notes in Computer Science},
abstract = {In this demonstration, we will present the concepts and an implementation of an inductive database \textendash as proposed by Imielinski and Mannila \textendash in the relational model. The goal is to support all steps of the knowledge discovery process on the basis of queries to a database system. The query language SiQL (structured inductive query language), an SQL extension, offers query primitives for feature selection, discretization, pattern mining, clustering, instance-based learning and rule induction. A prototype system processing such queries was implemented as part of the SINDBAD (structured inductive database development) project. To support the analysis of multi-relational data, we incorporated multi-relational distance measures based on set distances and recursive descent. The inclusion of rule-based classification models made it necessary to extend the data model and software architecture significantly. The prototype is applied to three different data sets: gene expression analysis, gene regulation prediction and structure-activity relationships (SARs) of small molecules.},
keywords = {data mining, inductive databases, machine learning, query languages},
pubstate = {published},
tppubtype = {inproceedings}
}
In this demonstration, we will present the concepts and an implementation of an inductive database – as proposed by Imielinski and Mannila – in the relational model. The goal is to support all steps of the knowledge discovery process on the basis of queries to a database system. The query language SiQL (structured inductive query language), an SQL extension, offers query primitives for feature selection, discretization, pattern mining, clustering, instance-based learning and rule induction. A prototype system processing such queries was implemented as part of the SINDBAD (structured inductive database development) project. To support the analysis of multi-relational data, we incorporated multi-relational distance measures based on set distances and recursive descent. The inclusion of rule-based classification models made it necessary to extend the data model and software architecture significantly. The prototype is applied to three different data sets: gene expression analysis, gene regulation prediction and structure-activity relationships (SARs) of small molecules.
4.
Richter, Lothar; Wicker, Jörg; Kessler, Kristina; Kramer, Stefan
An Inductive Database and Query Language in the Relational Model Proceedings Article
In: Proceedings of the 11th International Conference on Extending Database Technology: Advances in Database Technology, pp. 740–744, ACM, 2008, ISBN: 978-1-59593-926-5.
Abstract | Links | BibTeX | Altmetric | PlumX | Tags: data mining, inductive databases, machine learning, query languages
@inproceedings{richter2008inductive,
title = {An Inductive Database and Query Language in the Relational Model},
author = {Lothar Richter and J\"{o}rg Wicker and Kristina Kessler and Stefan Kramer},
url = {https://wicker.nz/nwp-acm/authorize.php?id=N10033
http://doi.acm.org/10.1145/1353343.1353440},
doi = {10.1145/1353343.1353440},
isbn = {978-1-59593-926-5},
year = {2008},
date = {2008-01-01},
booktitle = {Proceedings of the 11th International Conference on Extending Database Technology: Advances in Database Technology},
pages = {740--744},
publisher = {ACM},
series = {EDBT '08},
abstract = {In the demonstration, we will present the concepts and an implementation of an inductive database -- as proposed by Imielinski and Mannila -- in the relational model. The goal is to support all steps of the knowledge discovery process, from pre-processing via data mining to post-processing, on the basis of queries to a database system. The query language SIQL (structured inductive query language), an SQL extension, offers query primitives for feature selection, discretization, pattern mining, clustering, instance-based learning and rule induction. A prototype system processing such queries was implemented as part of the SINDBAD (structured inductive database development) project. Key concepts of this system, among others, are the closure of operators and distances between objects. To support the analysis of multi-relational data, we incorporated multi-relational distance measures based on set distances and recursive descent. The inclusion of rule-based classification models made it necessary to extend the data model and the software architecture significantly. The prototype is applied to three different applications: gene expression analysis, gene regulation prediction and structure-activity relationships (SARs) of small molecules.},
keywords = {data mining, inductive databases, machine learning, query languages},
pubstate = {published},
tppubtype = {inproceedings}
}
In the demonstration, we will present the concepts and an implementation of an inductive database — as proposed by Imielinski and Mannila — in the relational model. The goal is to support all steps of the knowledge discovery process, from pre-processing via data mining to post-processing, on the basis of queries to a database system. The query language SIQL (structured inductive query language), an SQL extension, offers query primitives for feature selection, discretization, pattern mining, clustering, instance-based learning and rule induction. A prototype system processing such queries was implemented as part of the SINDBAD (structured inductive database development) project. Key concepts of this system, among others, are the closure of operators and distances between objects. To support the analysis of multi-relational data, we incorporated multi-relational distance measures based on set distances and recursive descent. The inclusion of rule-based classification models made it necessary to extend the data model and the software architecture significantly. The prototype is applied to three different applications: gene expression analysis, gene regulation prediction and structure-activity relationships (SARs) of small molecules.
3.
Wicker, Jörg; Brosdau, Christoph; Richter, Lothar; Kramer, Stefan
SINDBAD SAILS: A Service Architecture for Inductive Learning Schemes Proceedings Article
In: Proceedings of the First Workshop on Third Generation Data Mining: Towards Service-Oriented Knowledge Discovery, 2008.
Abstract | Links | BibTeX | Tags: data mining, inductive databases, machine learning, query languages
@inproceedings{wicker2008sindbadsails,
title = {SINDBAD SAILS: A Service Architecture for Inductive Learning Schemes},
author = {J\"{o}rg Wicker and Christoph Brosdau and Lothar Richter and Stefan Kramer},
url = {http://www.ecmlpkdd2008.org/files/pdf/workshops/sokd/2.pdf},
year = {2008},
date = {2008-01-01},
booktitle = {Proceedings of the First Workshop on Third Generation Data Mining: Towards Service-Oriented Knowledge Discovery},
abstract = {The paper presents SINDBAD SAILS (Service Architecture for Inductive Learning Schemes), a Web Service interface to the inductive database SINDBAD. To the best of our knowledge, it is the first time a Web Service interface is provided for an inductive database. The combination of service-oriented architectures and inductive databases is particularly useful, as it enables distributed data mining without the need to install specialized data mining or machine learning software. Moreover, inductive queries can easily be used in almost any kind of programming language. The paper discusses the underlying concepts and explains a sample program making use of SINDBAD SAILS.},
keywords = {data mining, inductive databases, machine learning, query languages},
pubstate = {published},
tppubtype = {inproceedings}
}
The paper presents SINDBAD SAILS (Service Architecture for Inductive Learning Schemes), a Web Service interface to the inductive database SINDBAD. To the best of our knowledge, it is the first time a Web Service interface is provided for an inductive database. The combination of service-oriented architectures and inductive databases is particularly useful, as it enables distributed data mining without the need to install specialized data mining or machine learning software. Moreover, inductive queries can easily be used in almost any kind of programming language. The paper discusses the underlying concepts and explains a sample program making use of SINDBAD SAILS.
2.
Wicker, Jörg; Fenner, Kathrin; Ellis, Lynda; Wackett, Larry; Kramer, Stefan
Machine Learning and Data Mining Approaches to Biodegradation Pathway Prediction Proceedings Article
In: Bridewell, Will; Calders, Toon; 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 | Tags: biodegradation, cheminformatics, computational sustainability, enviPath, machine learning, metabolic pathways
@inproceedings{wicker2008machine,
title = {Machine Learning and Data Mining Approaches to Biodegradation Pathway Prediction},
author = {J\"{o}rg Wicker and Kathrin Fenner and Lynda Ellis and Larry Wackett and Stefan Kramer},
editor = {Will Bridewell and Toon Calders and Ana Karla Medeiros and Stefan Kramer and Mykola Pechenizkiy and Ljupco Todorovski},
url = {http://www.ecmlpkdd2008.org/files/pdf/workshops/ipm/9.pdf},
year = {2008},
date = {2008-01-01},
booktitle = {Proceedings of the Second International Workshop on the Induction of Process Models at ECML PKDD 2008},
keywords = {biodegradation, cheminformatics, computational sustainability, enviPath, machine learning, metabolic pathways},
pubstate = {published},
tppubtype = {inproceedings}
}
2006
1.
Kramer, Stefan; Aufschild, Volker; Hapfelmeier, Andreas; Jarasch, Alexander; Kessler, Kristina; Reckow, Stefan; Wicker, Jörg; Richter, Lothar
Inductive Databases in the Relational Model: The Data as the Bridge Proceedings Article
In: Bonchi, Francesco; Boulicaut, Jean-François (Ed.): Knowledge Discovery in Inductive Databases, pp. 124-138, Springer Berlin Heidelberg, 2006, ISBN: 978-3-540-33292-3.
Abstract | Links | BibTeX | Altmetric | PlumX | Tags: data mining, inductive databases, machine learning, query languages
@inproceedings{kramer2006inductive,
title = {Inductive Databases in the Relational Model: The Data as the Bridge},
author = {Stefan Kramer and Volker Aufschild and Andreas Hapfelmeier and Alexander Jarasch and Kristina Kessler and Stefan Reckow and J\"{o}rg Wicker and Lothar Richter},
editor = {Francesco Bonchi and Jean-Fran\c{c}ois Boulicaut},
url = {http://dx.doi.org/10.1007/11733492_8},
doi = {10.1007/11733492_8},
isbn = {978-3-540-33292-3},
year = {2006},
date = {2006-01-01},
booktitle = {Knowledge Discovery in Inductive Databases},
volume = {3933},
pages = {124-138},
publisher = {Springer Berlin Heidelberg},
series = {Lecture Notes in Computer Science},
abstract = {We present a new and comprehensive approach to inductive databases in the relational model. The main contribution is a new inductive query language extending SQL, with the goal of supporting the whole knowledge discovery process, from pre-processing via data mining to post-processing. A prototype system supporting the query language was developed in the SINDBAD (structured inductive database development) project. Setting aside models and focusing on distance-based and instance-based methods, closure can easily be achieved. An example scenario from the area of gene expression data analysis demonstrates the power and simplicity of the concept. We hope that this preliminary work will help to bring the fundamental issues, such as the integration of various pattern domains and data mining techniques, to the attention of the inductive database community.},
keywords = {data mining, inductive databases, machine learning, query languages},
pubstate = {published},
tppubtype = {inproceedings}
}
We present a new and comprehensive approach to inductive databases in the relational model. The main contribution is a new inductive query language extending SQL, with the goal of supporting the whole knowledge discovery process, from pre-processing via data mining to post-processing. A prototype system supporting the query language was developed in the SINDBAD (structured inductive database development) project. Setting aside models and focusing on distance-based and instance-based methods, closure can easily be achieved. An example scenario from the area of gene expression data analysis demonstrates the power and simplicity of the concept. We hope that this preliminary work will help to bring the fundamental issues, such as the integration of various pattern domains and data mining techniques, to the attention of the inductive database community.