LHA - Leipzig Health Atlas

We evaluated if the PhenoMan returns correct/complete result sets and if it is working with real data. To simulate a FHIR health data store with real data we used Synthea(TM) to generate a large data set and imported it into a HAPI FHIR JPA Server. Based on the synthetic data set we developed ten example queries with different structure and complexity with PhenoMan and SQL. We compared the results of the queries in means of execution time and equality of results.

The detailed steps of the evaluation
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This assay bundles all synthetic FHIR data of the PhenoMan evaluation. The data arised from a subset of a Synthea(TM) generated data set.
We truncated some resource types like Encounter and Provider to reduce the size of the data set and to speed up the import in a FHIR health data store.

We modelled the algorithm for determining Type 2 Diabetes Mellitus (T2DM) cases presented by PheKB.org using Phenotype Manager (PhenoMan).

T2DM Phenotype Algorithm Specification Ontology (PASO) developed using PhenoMan

The tabular representation of the T2DM phenotype algorithm generated by PhenoMan using the T2DM ontology

Example Reasoner Report generated by PhenoMan

Example Reasoner Report generated by PhenoMan

Example Reasoner Report generated by PhenoMan

The PanCancer Browser is a web application for the interactive comparison of molecular landscapes of different cancers provided by the oposSOM analysis pipeline. It complements the single data set-centered browsing tool oposSOM-Browser.

oposSOM is a comprehensive, machine learning based open-source data analysis software combining functionalities such as diversity analyses, biomarker selection, function mining, and visualization. These functionalities are now available as interactive web-browser application for a broader user audience interested in extracting detailed information from high-throughput omics data sets pre-processed by oposSOM. It enables interactive browsing of single-gene and gene set profiles, of molecular
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Core Ontology of Phenotypes. Contribute to Onto-Med/COP development by creating an account on GitHub.

The publication of research results is part of good scientific practice and is increasingly ex-pected and demanded. The FAIR data principles of data sharing are an important guideline - even if medical data sharing makes it necessary to define access restrictions.

The Leipzig Health Atlas (LHA) offers a platform for presentation and exchange of publications, (bio-) medical data, models and software tools
from the field of health research. The LHA is based on the FAIR Data Principles (Findable, Accessible, Interoperable, Reusable). Currently,
more than 18 research consortia contribute data from the domains lymphoma, glioma, sepsis, hereditary colorectal and breast cancer, and
others. Target groups are clinicians, epidemiologists, molecular geneticists, human
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The need to manage research data is recognised by the research community - sponsors, legislators, publishers expect and encourage good scientific practice, which not only includes archiving, but also the availability of research data and results in line with FAIR principles.
The publication gives a brief overview of the Leipzig Health Atlas (LHA), which is a project for the presentation and exchange of a wide range of publications, (bio) medical data (e.g. clinical, epidemiological, molecular),
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The need to manage research data is recognised by the research community - sponsors, legislators, publishers expect and encourage good scientific practice, which not only includes archiving, but also the availability of research data and results in line with FAIR principles.
The publication gives a brief overview of the Leipzig Health Atlas (LHA), which is a project for the presentation and exchange of a wide range of publications, (bio) medical data (e.g. clinical, epidemiological, molecular),
...

Presentation on JOWO/ODLS 2019 in Graz

The conference will focus on current contributions to research data management in Saxony. On the basis of practical reports and a poster session, it will be shown what research data management can achieve and what advantages it can offer. An advanced training course on data management planning will help to explore the possibilities and challenges of the FDM for oneself and for research projects. Finally, important aspects and questions will be discussed with each other and with experts at several
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