LHA

The Leipzig Health Atlas (LHA) is an alliance of medical ontologists, medical systems biologists and clinical trials groups to design and implement a multi-functional and quality-assured atlas. It provides models, data and metadata on specific use cases from medical research fields in which our team has scientific and clinical expertise. Two basic characteristics are:

  1. an interoperable ontology-based semantic platform to share highly annotated data, novel ontologies, usable models and working software tools; 
  2. an advanced, application-oriented analytic pipeline for a clinical and scientific user community to provide disease-related phenotype classifications, omics based disease sub-classifications, risk predictions and simulation models for diseases and organ functions

How to use the Leipzig Health Atlas

Currently, we provide the following content and services:

Scientific projects

» List of scientific projects contained in the LHA.

Data sets

» Clinical data sets, OMICS data sets and SOM data sets for download.

Models

» Models such as algorithm-based prediction or simulation models.

Publications

» Paper resulting from our work.

Tools and services

» Cohort Section Tool (i2b2)
» Basic Analysis Tool (tranSMART)
» Metadata Browser (MDR)

Scientific projects within the LHA

» Project Area 1: Semantic Data Integration, Ontologies and mining services
» Project Area 2: Application Development and Validation
» Project Area 3: Application Integration and Community Construction
» Project Area 4: Management

Latest Publications

User Expectations of Metadata Repositories for Clinical Research

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Authors
Metadata Repositories (MDR) are databases for data elements that can be utilized in research as well as in medical care. These data elements are not the actual patient data (facts), but a complete definition of the variables or characteristics used, including coding, unit of measurement, data type and other aspects. The aim of the project described here was to evaluate possible application scenarios for MDRs by a larger group of experts. The focus was not on specific software, but on the community's basic expectation of such a database of data elements.

Ontology-Based Modelling of Web Content: Example Leipzig Health Atlas

Publication Date
The realisation of a complex web portal, including the modelling of content, is a challenging process. The contents describe different interconnected entities that form a complex structure. The entities and their relations have to be systematically analysed, and the content has to be specified and integrated into a content management system (CMS). Ontologies provide a suitable solution for modelling and specifying complex entities and their relations.

Pseudotime dynamics in melanoma single-cell transcriptomes reveals different mechanisms of tumor progression

Publication Date
Single-cell transcriptomics has been used for analysis of heterogeneous populations of cells during developmental processes and for analysis of tumor cell heterogeneity. More recently, analysis of pseudotime (PT) dynamics of heterogeneous cell populations has been established as a powerful concept to study developmental processes. Here we perform PT analysis of 3 melanoma short-term cultures with different genetic backgrounds to study specific and concordant properties of PT dynamics of selected cellular programs with impact on melanoma progression.

Towards Phenotyping of Clinical Trial Eligibility Criteria

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Background: Medical plaintext documents contain important facts about patients, but they are rarely available for structured queries. The provision of structured information from natural language texts in addition to the existing structured data can significantly speed up the search for fulfilled inclusion criteria and thus improve the recruitment rate. Objectives: This work is aimed at supporting clinical trial recruitment with text mining techniques to identify suitable subjects in hospitals.

Diabetes and hypertension contribute to normal-appearing white matter microstructural variability in the brain

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Background and objectives: Obesity has been associated with increased risk of dementia. Grey and white matter (WM) of the brain are commonly used as biomarkers for early detection of dementia. However, considering WM, available neuroimaging studies had mainly small sample size and yielded less conclusive results (Kullmann et al., 2015). Recently, a positive correlation between obesity and fractional anisotropy (FA) in a middle age group was reported (Birdsill et al. 2017).