Volume 59, Issue 4 - Data Integration in Life Sciences / Guest Editor: Hans-Ulrich Prokosch

It is usually a challenging task to integrate and analyze huge amounts of heterogeneous data in large medical research projects. Often meaningful new insights can be achieved by visualizing medical data on geographical maps. For instance in epidemiological studies, data is often explored on a spatial dimension. LIFE is a large epidemiological study, managed by the LIFE Research Center for Civilization Diseases at Leipzig University. The study investigates the health-related states of the local population, e.g. by looking at the role of lifestyle factors on major civilization diseases. To allow for an effective data exploration, the development of sophisticated data analysis and spatial visualization techniques is necessary. Here, we present the interactive web application LIFE Spatial Data Visualization System (LIFE-SDVS) that adds a geographical facet to the data integration and analysis workflow of the LIFE research project.

Vast amounts of medical information are still recorded as unstructured text. The knowledge contained in this textual data has a great potential to improve clinical routine care, to support clinical research, and to advance personalization of medicine. To access this knowledge, the underlying data has to be semantically integrated – an essential prerequisite to which is information extraction from clinical documents. A body of work, and a good selection of openly available tools for information extraction and semantic integration in the medical domain exist, yet almost exclusively for English language documents. For German texts the situation is rather different: research work is sparse, tools are proprietary or unpublished, and rarely any freely available textual resources exist. In this survey, we (1) describe the challenges of information extraction from German medical documents and the hurdles posed to research in this area, (2) especially address the problems of missing German language resources and privacy implications, and (3) identify the steps necessary to overcome these hurdles and fuel research in semantic integration of textual clinical data.

Modelling and analysis of metabolic processes is an established research field in Computational Systems Biology and Medicine. There are many different approaches for modelling as well as for visualization, but often these methods ignore the subcellular localization of proteins, enzymes and transporters involved in the metabolic processes. Over the recent years, we developed two tools: CELLmicrocosmos 4 PathwayIntegration (CmPI) and VANTED. CmPI is used to analyse, visualize and explore potential subcellular localizations in a cell environment. CmPI provides import capabilities for metabolic pathways and simple visualization of directed networks showing the enzyme-compound relationships inside this environment in 2D as well as 3D. VANTED allows to analyse and visualize biological networks, to integrate data into these networks and simulate their dynamics. Here, we will discuss future perspectives of these approaches and how to extend the capabilities of CmPI's localization method to provide a) complex modelling, visualization and exploration of metabolic networks in combination with -omics data, and b) immersive analytics of the spatially distributed networks using different Virtual Reality-based technologies. We discuss the prototypic integration of CmPI with VANTED and the large display visualization framework Omegalib in combination with a semi-immersive monitor zSpace ™ and CAVE2 ™ .

In this opinion paper we provide an overview of some challenges concerning data provenance in biomedical research. We reflect current literature and depict some examples of existing implicit or explicit provenance aspects in some standard data types in translational research. Furthermore, we assess the need of further data provenance standardization in biomedical informatics. Basic data provenance should provide a recall about the origin of the data, transformation process steps, support replication and presentation of the data. Even though usable concepts for the documentation of data provenance can be found in other fields as early as 2005, the penetration rate in biomedical projects and in the biomedical literature is quite low. The awareness for the necessity of basic data provenance has to be raised, the education of data managers has to be further improved.

Our work on narrowing the gap between verification and systematic testing has two directions: (1) complementing verification with systematic testing, and (2) pushing systematic testing toward reaching verification. In the first direction, we explore how to effectively combine static analysis with systematic testing, so as to guide test generation toward properties that have not been previously checked by a static analyzer in a sound way. This combination significantly reduces the test effort while checking more unverified properties. In the second direction, we push systematic testing toward checking as many executions as possible of a real and complex image parser, so as to prove the absence of a certain class of errors. This verification attempt required no static analysis or source code annotations; our purely dynamic techniques targeted the verification of the parser implementation, including complicated assembly patterns that most static analyses cannot handle.