Robert Reihs

Analysis of biomedical data with multilevel glyphs

BackgroundThis paper presents multilevel data glyphs optimized for the interactive knowledge discovery and visualization of large biomedical data sets. Data glyphs are three- dimensional objects defined by multiple levels of geometric descriptions (levels of detail) combined with a mapping of data attributes to graphical elements and methods, which specify their spatial position.MethodsIn the data mapping phase, which is done by a biomedical expert, meta information about the data attributes (scale, number of distinct values) are compared with the visual capabilities of the graphical elements in order to give a feedback to the user about the correctness of the variable mapping. The spatial arrangement of glyphs is done in a dimetric view, which leads to high data density, a simplified 3D navigation and avoids perspective distortion.ResultsWe show the usage of data glyphs in the disease analyser a visual analytics application for personalized medicine and provide an outlook to a biomedical web visualization scenario.ConclusionsData glyphs can be successfully applied in the disease analyser for the analysis of big medical data sets. Especially the automatic validation of the data mapping, selection of subgroups within histograms and the visual comparison of the value distributions were seen by experts as an important functionality.

State-of-the-Art and Future Challenges in the Integration of Biobank Catalogues

Biobanks are essential for the realization of P4-medicine, hence indispensable for smart health. One of the grand challenges in biobank research is to close the research cycle in such a way that all the data generated by one research study can be consistently associated to the original samples, therefore data and knowledge can be reused in other studies. A catalogue must provide the information hub connecting all relevant information sources. The key knowledge embedded in a biobank catalogue is the availability and quality of proper samples to perform a research project. Depending on the study type, the samples can reflect a healthy reference population, a cross sectional representation of a certain group of people (healthy or with various diseases) or a certain disease type or stage. To overview and compare collections from different catalogues, we introduce visual analytics techniques, especially glyph based visualization techniques, which were successfully applied for knowledge discovery of single biobank catalogues. In this paper, we describe the state-of-the art in the integration of biobank catalogues addressing the challenge of combining heterogeneous data sources in a unified and meaningful way, consequently enabling the discovery and visualization of data from different sources. Finally we present open questions both in data integration and visualization of unified catalogues and propose future research in data integration with a linked data approach and the fusion of multi level glyph and network visualization.

Connecting Genes with Diseases

This paper presents a visual data mining approach using the combination of clinical data, pathways and gene-expression data. The visual exploration of medical data using pathways to navigate and filter the data allows a more systematic and efficient investigation of problems in modern life science. A multiplicity of hypothesis can be evaluated in the same period of time, enabling a much better exploitation of the data. We present a system for data preprocessing and automatic classification, a set of visualization views and finally the integration in the Caleydo visualization framework, which enables the “coupling” of genetic and a broad spectrum of clinical data. With the help of the Caleydo framework the medical expert can identify connections between genetic parameters, patient subgroups, and drug responses.

The RD-Connect Registry & Biobank Finder: a tool for sharing aggregated data and metadata among rare disease researchers

In rare disease (RD) research, there is a huge need to systematically collect biomaterials, phenotypic, and genomic data in a standardized way and to make them findable, accessible, interoperable and reusable (FAIR). RD-Connect is a 6 years global infrastructure project initiated in November 2012 that links genomic data with patient registries, biobanks, and clinical bioinformatics tools to create a central research resource for RDs. Here, we present RD-Connect Registry & Biobank Finder, a tool that helps RD researchers to find RD biobanks and registries and provide information on the availability and accessibility of content in each database. The finder concentrates information that is currently sparse on different repositories (inventories, websites, scientific journals, technical reports, etc.), including aggregated data and metadata from participating databases. Aggregated data provided by the finder, if appropriately checked, can be used by researchers who are trying to estimate the prevalence of a RD, to organize a clinical trial on a RD, or to estimate the volume of patients seen by different clinical centers. The finder is also a portal to other RD-Connect tools, providing a link to the RD-Connect Sample Catalogue, a large inventory of RD biological samples available in participating biobanks for RD research. There are several kinds of users and potential uses for the RD-Connect Registry & Biobank Finder, including researchers collaborating with academia and the industry, dealing with the questions of basic, translational, and/or clinical research. As of November 2017, the finder is populated with aggregated data for 222 registries and 21 biobanks.

From the evaluation of existing solutions to an all-inclusive package for biobanks

The domain of biobanking has gone through many stages and as a result there are a wide range of commercial and open source software solutions available. The utilization of these software tools requires different levels of domain and technical skills for installation, configuration and ultimate us of these biobank software tools. To compound this complexity the biobanking community are required to work together in order to share knowledge and jointly build solutions to underpin the research infrastructure. We have evaluated the available tools, described them in a catalogue (BiobankApps) and made a selection of tools available to biobanks in a reference toolbox (BIBBOX) that are use-case driven. In the BiobankApps tool catalogue, both commercial and open source software solutions related to the biobanking domain are included, classified and evaluated. The evaluation covers: 1) “user review” by an authenticated user 2) domain expert: quick analysis by BBMRI members and 3) domain expert: detailed analysis and test installation with real world data. The evaluation is paired with a survey across the more “advanced” (from a technology perspective) biobanks to investigate what tools are currently used and summarises known benefits/drawbacks of the respective packages. In the second step we recommend tools for specific use cases, and install, configure and connect these in the BIBBOX framework. This service also builds on the existing work in the United Kingdom in seeking to establish the motivations for different stakeholders to become involved and therefore assisting in prioritising the use-cases based on the level of need and support within the research community. All tools associated to a use-case are available as BIBBOX applications (technically this is achieved by docker containers), which are integrated in the BIBBOX framework with central identification and user management. In future work we plan to share the acquired knowledge with other networks, develop an Application Programmable Interface (API) for the exchange of metadata with other tool catalogues and work on an ontology for the evaluation of biobank software.

Machine Learning and Knowledge Extraction in Digital Pathology Needs an Integrative Approach

During the last decade pathology has benefited from the rapid progress of image digitizing technologies, which led to the development of scanners, capable to produce so-called Whole Slide images (WSI) which can be explored by a pathologist on a computer screen comparable to the conventional microscope and can be used for diagnostics, research, archiving and also education and training. Digital pathology is not just the transformation of the classical microscopic analysis of histological slides by pathologists to just a digital visualization. It is a disruptive innovation that will dramatically change medical work-flows in the coming years and help to foster personalized medicine. Really powerful gets a pathologist if she/he is augmented by machine learning, e.g. by support vector machines, random forests and deep learning. The ultimate benefit of digital pathology is to enable to learn, to extract knowledge and to make predictions from a combination of heterogenous data, i.e. the histological image, the patient history and the *omics data. These challenges call for integrated/integrative machine learning approach fostering transparency, trust, acceptance and the ability to explain step-by-step why a decision has been made.

Adaptive Visual Symbols for Personal Health Records

As a hub of information controlled by the patient, personal health records (PHR) collect information from the patient medical history including a wide variety of data sources as patients observations, lab results, clinical findings and in the future maybe even personal genetic data and automatic recordings from monitoring devices. This development will on the one hand make health care more personalized and user controlled but on the other hand also overloads consumers with a huge amount of data. To address this issue we developed a framework for adaptive visual symbols (AVS). An AVS can adapt its appearance and level of detail during the communication process. Finally we demonstrate the AVS principle for the visualization of personal health records.

Design, User Experience and Usability Requirements for NGS Workflows in Clinical Applications

Next-generation sequencing (NGS) has transformed genomic research by decreasing the cost of sequencing and increasing the throughput. Today, translation of NGS technology to (cancer) diagnostics and therapeutics is widely anticipated. In this paper we give an overview of design, user experience and usability (DUXU) requirements for NGS applications with a special focus on clinical and diagnostic settings.

Data Driven GUI Design and Visualization for a NGS Based Clinical Decision Support System.

Next-generation sequencing (NGS) has transformed genomic research by decreasing the cost of sequencing and increasing the throughput. Today the translation of NGS technology to (cancer) diagnostics and therapeutics is widely anticipated. In this paper we give an overview of design, user experience and usability (DUXU) requirements for NGS applications with a special focus on clinical and diagnostic settings and describe a data driven GUI and visualization framework for a NGS based clinical decision support system (DSS). In such a DSS it is hard to oversee the huge amount of information, especially if given only in textual form. To overcome this we developed a visual language for electronic health records (EHR) for navigation within a case and comparison of medical cases.

Supporting the development of biobanks in low and medium income countries

Biobanks are an organized collection of biological material and associated data. They are a fundamental resource for life science research and contribute to the development of pharmaceutical drugs, diagnostic markers and to a deeper understanding of the genetics that regulate the development of all life on earth. Biobanks are well established in High Income Countries (HIC) and are rapidly emerging in Low and Middle Income Countries (LMIC). Surveys among biobanks operating in a LMIC setting indicate that limited resources and short term funding tied to specific projects threaten the sustainability of the biobanks. Fit-for-purpose biobanks targeting major societal challenges such as HIV and Malaria provide an excellent basis for integrating biobanks with the available research communities in LMIC regions. But to become sustainable for the future it is important that biobanks become an integrated part of local research communities. To achieve this, the cost of operating biobanks must be lowered, templates must be developed to support local ethics committees and researchers must be given the opportunity to build experience in successfully operating biobank based research projects. The B3Africa consortium is based on these conclusions and set up to support biobank based research by creating a cost efficient Laboratory Information Management System (LIMS) for developing biobanks and also contribute to the training and capacity building in the local research community. The technical platform called the eB3Kit is open source and consists of a LIMS and a bioinformatics module based on the eBiokit that allow researchers to take control over the analysis of their own data. Along with the technical platform the consortium will also contribute training and support for the associated infrastructures necessary to regulate the ethical and legal implications of biobank based research.