Konrad Stark

The Genome Austria Tissue Bank (GATiB).

In the context of the Austrian Genome Program, a tissue bank is being established (Genome Austria Tissue Bank, GATiB) which is based on a collection of diseased and corresponding normal tissues representing a great variety of diseases at their natural frequency of occurrence from a non-selected Central European population of more than 700,000 patients. Major emphasis is put on annotation of archival tissue with comprehensive clinical data, including follow-up data. A specific IT infrastructure supports sample annotation, tracking of sample usage as well as sample and data storage. Innovative data protection tools were developed which prevent sample donor re-identification, particularly if detailed medical and genetic data are combined. For quality control of old archival tissues, new techniques were established to check RNA quality and antigen stability. Since 2003, GATiB has changed from a population-based tissue bank to a disease-focused biobank comprising major cancers such as colon, breast, liver, as well as metabolic liver diseases and organs affected by the metabolic syndrome. Prospectively collected tissues are associated with blood samples and detailed data on the sample donor’s disease, lifestyle and environmental exposure, following standard operating procedures. Major emphasis is also placed on ethical, legal and social issues (ELSI) related to biobanks. A specific research project and an international advisory board ensure the proper embedding of GATiB in society and facilitate international networking.

Information Systems for Federated Biobanks

Biobanks store and manage collections of biological material (tissue, blood, cell cultures, etc.) and manage the medical and biological data associated with this material. Biobanks are invaluable resources for medical research. The diversity, heterogeneity and volatility of the domain make information systems for biobanks a challenging application domain. Information systems for biobanks are foremost integration projects of heterogenous fast evolving sources. The European project BBMRI (Biobanking and Biomolecular Resources Research Infrastructure) has the mission to network European biobanks, to improve resources for biomedical research, an thus contribute to improve the prevention, diagnosis and treatment of diseases. We present the challenges for interconnecting European biobanks and harmonizing their data. We discuss some solutions for searching for biological resources, for managing provenance and guaranteeing anonymity of donors. Furthermore, we show how to support the exploitation of such a resource in medical studies with specialized CSCW tools.

Priority-Based k-anonymity accomplished by weighted generalisation structures

Biobanks are gaining in importance by storing large collections of patient’s clinical data (e.g. disease history, laboratory parameters, diagnosis, life style) together with biological materials such as tissue samples, blood or other body fluids. When releasing these patient-specific data for medical studies privacy protection has to be guaranteed for ethical and legal reasons. k-anonymity may be used to ensure privacy by generalising and suppressing attributes in order to release sufficient data twins that mask patients’ identities. However, data transformation techniques like generalisation may produce anonymised data unusable for medical studies because some attributes become too coarse-grained. We propose a priority-driven anonymisation technique that allows to specify the degree of acceptable information loss for each attribute separately. We use generalisation and suppression of attributes together with a weighting-scheme for quantifying generalisation steps. Our approach handles both numerical and categorical attributes and provides a data anonymisation based on priorities and weights. The anonymisation algorithm described in this paper has been implemented and tested on a carcinoma data set. We discuss some general privacy protecting methods for medical data and show some medical-relevant use cases that benefit from our anonymisation technique.

GATiB-CSCW, Medical Research Supported by a Service-Oriented Collaborative System

Medical research is a collaborative process in an interdisciplinary environment that may be effectively supported by a Computer Supported Cooperative Work (CSCW) system. Such a system imposes specific requirements in order to allow flexible integration of data, analysis services and communication mechanisms. Persons with different expertise and access rights cooperate in mutually influencing contexts (e.g. clinical studies, research cooperations). Thus, appropriate virtual environments are needed to facilitate context-aware communication, deployment of biomedical tools as well as data and knowledge sharing. We systematically elaborate the main requirements of a medical CSCW system and present a conceptual model, as well as an architectural proposal satisfying the demands. We design a prototypical virtual workbench to support research and routine activities in the context of the GATiB (Genome Austria Tissue Bank) initiative.

Achieving k-anonymity in DataMarts used for gene expressions exploitation

Abstract Gene expression profiling is a sophisticated method to discover differences in activation patterns of genes between different patient collectives. By reasonably defining patient groups from a medical point of view, subsequent gene expression analysis may reveal disease-related gene expression patterns that are applicable for tumor markers and pharmacological target identification. When releasing patient-specific data for medical studies privacy protection has to be guaranteed for ethical and legal reasons. k-anonymisation may be used to generate a sufficient number of k data twins in order to ensure that sensitive data used in analyses is protected from being linked to individuals. We use an adapted concept of k-anonymity for distributed data sources and include various customisation parameters in the anonymisation process to guarantee that the transformed data is still applicable for further processing. We present a real-world medical-relevant use case and show how the related data is materialised, anonymised, and released in a data mart for testing the related hypotheses.

Data Management for Federated Biobanks

Biobanks store and manage collections of biological material (tissue, blood, cell cultures, etc.) and manage the medical and biological data associated with this material. Biobanks are invaluable resources for medical research. The diversity, heterogeneity and volatility of the domain make information systems for biobanks a challenging application domain. The European project BBMRI (Biobanking and Biomolecular Resources Research Infrastructure) has the mission to network European biobanks, to improve resources for biomedical research, an thus contribute to improve the prevention, diagnosis and treatment of diseases. We present the challenges and discuss some architectures for interconnecting European biobanks and harmonizing their data.

Enhanced security management for flexible and dynamic cooperative environments

This paper deals with the challenge to create an authorization and authentication infrastructure for virtual knowledge spaces. Virtual knowledge spaces are a concept to build up flexible and adjustable environments for cooperative work and learning processes. When developing authorization concepts for virtual knowledge spaces, different boundary conditions have to be considered. Basically, we identify the specific requirements for the creation of coherent and intuitive rules to reduce the administrative complexity to a minimum and prevent mistakes. Furthermore we turn attention to flexibility of the authorization infrastructure as well as performance issues. The rights management described in this paper ought to fulfill these requirements.

Incorporating data provenance in a medical CSCW system

Medical research is a highly collaborative process in an interdisciplinary environment that may be effectively supported by a Computer Supported Cooperative Work (CSCW) system. Research activities should be traceable in order to allow verification of results, repeatability of experiments and documentation as learning processes. Therefore, by recording the provenance of data together with the collaborative context it is embedded into, novel types of provenance queries may be answered. We designed and implemented a next-generation CSCW system providing both the collaborative functionalities as well as the definition and execution of structured processes. We integrated a data provenance model recording process- and collaboration-related operations automatically and demonstrate the capabilities of the model by answering specific data provenance queries from the biomedical domain.