Semantic Harmonization of Alzheimer's Disease Datasets Using AD-Mapper.

Background: Despite numerous past endeavors for the semantic harmonization of Alzheimer's disease (AD) cohort studies, an automatic tool has yet to be developed.

Objective: As cohort studies form the basis of data-driven analysis, harmonizing them is crucial for cross-cohort analysis. We aimed to accelerate this task by constructing an automatic harmonization tool.

Deep Learning-based detection of psychiatric attributes from German mental health records.

Background: Health care records provide large amounts of data with real-world and longitudinal aspects, which is advantageous for predictive analyses and improvements in personalized medicine. Text-based records are a main source of information in mental health. Therefore, application of text mining to the electronic health records - especially mental state examination - is a key approach for detection of psychiatric disease phenotypes that relate to treatment outcomes.

STonKGs: a sophisticated transformer trained on biomedical text and knowledge graphs.

Motivation: The majority of biomedical knowledge is stored in structured databases or as unstructured text in scientific publications. This vast amount of information has led to numerous machine learning-based biological applications using either text through natural language processing (NLP) or structured data through knowledge graph embedding models. However, representations based on a single modality are inherently limited.

The COVID-19 Ontology.

Motivation: The COVID-19 pandemic has prompted an impressive, worldwide response by the academic community. In order to support text mining approaches as well as data description, linking and harmonization in the context of COVID-19, we have developed an ontology representing major novel coronavirus (SARS-CoV-2) entities. The ontology has a strong scope on chemical entities suited for drug repurposing, as this is a major target of ongoing COVID-19 therapeutic development.