Project Wizard

Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes.

The complexity of cellular networks is an outstanding challenge for documentation, visualisation and mathematical modelling. In this project, we develop a new way to describe these networks that minimises the combinatorial complexity and allows an automatic visualisation and export of mathematical (ODE/rulebased) models.

Features:

• Automatic visualiztion with Cytoscape.
• Automatic generation of rule based models for BioNetGen.
• Storage of biological facts that can be used for modelling.

OpenEMed is a set of distributed healthcare information service components built around the OMG distributed object specifications and the HL7 (and other) data standards and is written in Java for platform portability. We emphasize the interoperable service functionality that this approach provides in reducing the time it takes to build a healthcare related system. It is not intended as a turnkey system but rather a set of components that can be assembled and configured to meet a variety of tasks.

MRIdb is an end-to-end data management system for MRI, combining the DCM4CHEE DICOM server with a bespoke front-end packaged into an easily deployable virtual machine. It interfaces directly with MRI scanners and handles image storage, retrieval and export. It provides role-based access control, patient-study assignment, and extensive auditing. MRIdb is the result of an ongoing collaboration between the BSS and the Imaging Sciences Department of Imperial College.