JGU WEKA REST Service

Webservice to WEKA Machine Learning Algorithms
RESTful API Webservice to WEKA Machine Learning Algorithms. This webservice provides an OpenRiskNet compliant REST interface to machine learning algorithms from the WEKA Java Library. This application is developed by the Institute of Computer Science at the Johannes Gutenberg University Mainz.

For developers
For end-users
Type:
Trained model, Model generation tool, Model, Service
API Type:
REST, OpenAPI, REST under OAS3 specification, OAS3
Categories:
API Definitions for OpenRiskNet applications and data, Processing and analysis
Applicability domain:
Computational modelling, Predictive toxicology
Topic:
Predictive modelling
Targeted industry:
Other consumer products
Targeted users:
Software Developers, Informed public, Researchers
Relevant OpenRiskNet case studies:
  • MetaP - Metabolism Prediction
  • ModelRX - Modelling for Prediction or Read Across

Provided by:
Johannes Gutenberg University Mainz
Contact:
Stefan Kramer
Licence:
GNU Lesser General Public License 3 (LGPLv3.0)
Login required:
No
Implementation status:
Available as web service, Containerised, API documentation available (Swagger-OpenAPI v2), Application programming interface available, OAS v3
Integration status:
Integrated application
Service integration operations completed:
Utilises the OpenRiskNet APIs to ensure that each service is accessible to our proposed interoperability layer.
Is annotated according to the semantic interoperability layer concept using defined ontologies.
Is containerised for easy deployment in virtual environments of OpenRiskNet instances.
Has documented scientific and technical background.
Is deployed into the OpenRiskNet reference environment.
Is listed in the OpenRiskNet discovery services.
Is listed in other central repositories like eInfraCentral, bio.tools and TeSS (ELIXIR).
Provides legal and ethical statements on how the service can be used.

Resources & Training

Poster
OpenRiskNet Part IV: WEKA Machine Learning Services for the Prediction of Half-Lifes of Chemicals and Nanoparticle Transport
Stefan Kramer, Denis Gebele, Atif Raza
28 Aug 2019
Abstract:
The OpenRiskNet project (https://openrisknet.org/) is funded by the H2020-EINFRA-22-2016 Programme and its main objective is the development of an open e-infrastructure providing data and software resources and services to a variety of industries requiring risk assessment (e.g. chemicals, cosmetic ingredients, pharma or nanotechnologies). We will present the WEKA machine learning services within the infrastructure and how they can be used to solve complex prediction tasks: the prediction of (i) half-life of chemicals under given environmental conditions and of (ii) nanoparticle transport behavior from physicochemical properties. For that purpose, we will reconstruct previous efforts using complex workflows and architectures and simplify the models while maintaining their prediction performance. In both cases, the overall problem (predicting the fate of a compound depending on its properties and external conditions) is modeled as a cascaded prediction model, where the prediction of one model is, with particular attention to validity and performance, entering another model as input. The approach performs well on the half-life data, while the nanoparticle data are too noisy and incomplete to warrant more than the most basic models. Overall, the reconstruction of the two applications within OpenRiskNet provides more evidence for the power and versatility of the framework.
Related services:
JGU WEKA REST Service

Target audience: Risk assessors, Researchers, Nanosafety community, Data modellers
Open access: yes
Licence: Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
Organisations involved: JGU
Poster
Report
Case Study description - Modelling for Prediction or Read Across [ModelRX]
28 Jun 2019
Abstract:
A training data set will be obtained from an OpenRiskNet data source. The model has then to be trained with OpenRiskNet modelling tools and the resulting model has to be packaged into a container, documented and ontologically annotated. The model will be validated using OECD guidelines. Finally, a prediction can be run.
Additional materials:
Case Study report

Target audience: OpenRiskNet stakeholders
Open access: yes
Licence: Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
Organisations involved: NTUA
Report
Report
Case Study description - Metabolism Prediction [MetaP]
9 May 2019
Abstract:
Metabolites may well play an important role in adverse effects of parent drug (or other xenobiotic) compounds. In this case study VU (CS leader), HITeC/HHU (associate partner and implementation challenge winner), JGU and UU work together on making methods and tools available for metabolite and site-of-metabolism (SOM) prediction. For that purpose we use and integrate ligand-based metabolite predictors (e.g. MetPred, enviPath, FAME, SMARTCyp) and we will incorporate protein-structure and -dynamics based approaches to predict the site of metabolism (SOM) by Cytochrome P450 enzymes (CYP450s). CYP450s metabolise ~75% of the currently marketed drugs and their active-site shape and plasticity often play an important role in determining the substrate’s SOM. We will also make services available for the prediction of microbial biotransformation pathways. During method development, model calibration and validation we will use data from XMetDB and other open-access databases for drugs, xenobiotics and their respective metabolites. To facilitate the combined use of the metabolite prediction approaches and their outcomes, we will benefit of ongoing development in workflow management systems (Nextflow, Squonk, MDStudio) and we will explore integration into and application of these platforms. Once integrated the added value of multiple predictors will be subject of a pilot study on consensus metabolite prediction.
Additional materials:
Case Study report
Related services:
JGU WEKA REST Service

Publisher: OpenRiskNet
Target audience: Risk assessors, OpenRiskNet stakeholders, Bioinformaticians
Open access: yes
Licence: Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
Organisations involved: JGU, UU, VU
Report
Presentation
WP4 Service Integration
13 Dec 2018
Additional materials:
WP4 presentation

Open access: yes
Licence: Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
Organisations involved: EwC, JGU, CRG, UM, UoB, NTUA, Fraunhofer, UU, VU, IM, INERIS
Presentation
Presentation
WP2 Interoperability, Deployment and Security
13 Dec 2018
Additional materials:
WP2 presentation
Related services:
JGU WEKA REST Service

Open access: yes
Licence: Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
Organisations involved: EwC, JGU, CRG, UM, UoB, NTUA, Fraunhofer, UU, VU, IM, INERIS
Presentation
Report
Initial API version provided to providers of services (Deliverable 2.2)
Rautenberg, Micha; Karwath, Andreas; Kramer, Stefan; Dudgeon, Tim; Spjuth, Ola; Bachler, Daniel; Exner, Thomas; Dokler, Joh; Sarimveis, Haralambos; Valsamis, Angelos; Doganis, Philip; Willighagen, Egon; Bois, Frederic
7 Nov 2018
Abstract:
This document reports the work towards the first version of the OpenRiskNet application programming interfaces (APIs) to be released to all partners of the consortium and associated partners for feedback and usage. Based on the diversity of the requirement foreseeable when developing the case studies to validate the infrastructure with real-world applications across all areas of predictive toxicology and risk assessment, a bottom-up approach to start with existing APIs and then harmonize them and bring them collectively to higher levels by integrating richer scientific annotation (semantic interoperability layer) was adopted in contrast to a top-down approach, where the API specification is defined by the consortium first and then all services have to be changed to comply to this specification.

Publisher: OpenRiskNet
Target audience: Developers, Software developers
Open access: yes
Licence: Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
Organisations involved: EwC, JGU, UM, NTUA, UU, IM, INERIS
Report
Public communication
OpenRiskNet Kicked off
Thomas Exner
24 Jan 2017
Abstract:
OpenRiskNet kicked off at Technology Park in Basel, Switzerland (on 15 and 16 December 2016). All partners were present on this two-day event.
Related services:
JGU WEKA REST Service

Publisher: OpenRiskNet
Target audience: OpenRiskNet stakeholders
Open access: yes
Licence: Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
Organisations involved: EwC, JGU, CRG, UM, UoB, NTUA, Fraunhofer, UU, VU, IM, INERIS
Public communication