Related Experiment Video
Updated: Jul 9, 2026
11:22
Automated Robotic Liquid Handling Assembly of Modular DNA Devices
Published on: December 1, 2017
Automation of in-silico data analysis processes through workflow management systems
1Bioinformatics, Istituto Nazionale per la Ricerca sul Cancro, Largo Rosanna Benzi 10, I-16132, (IST), Genova, Italy. paolo.romano@istge.it
Briefings in Bioinformatics
|December 7, 2007
Summary
Biological data integration faces challenges due to data volume and heterogeneity. This review explores a methodology using Extensible Markup Language (XML), Web Services, and Workflow Management Systems (WMS) for effective biomedical data mining.
Area of Science:
- Bioinformatics and Computational Biology
- Biomedical Data Science
Background:
- Vast amounts of biological data necessitate effective integration for data mining.
- Existing data integration systems face limitations due to data volume, heterogeneity, and distribution.
Purpose of the Study:
- To review a methodology for biomedical data integration.
- To explore the use of Extensible Markup Language (XML), Web Services, and Workflow Management Systems (WMS) for flexible and extensible data integration.
Main Methods:
- Leveraging network services for data access.
- Utilizing Extensible Markup Language (XML) for data representation.
- Employing Web Services for interoperability.
- Implementing Workflow Management Systems (WMS) for process orchestration.
Main Results:
- Many XML languages and Web Services for bioinformatics are available.
- Several Workflow Management Systems (WMS) have been proposed.
- The reviewed methodology offers a flexible and extensible approach to data integration.
Conclusions:
- The integration methodology based on XML, Web Services, and WMS addresses current limitations in biomedical data integration.
- Further development is needed to overcome existing limitations and enhance the methodology.