The new protein topology graph library web server.

SUMMARY: We present a new, extended version of the Protein Topology Graph Library web server. The Protein Topology Graph Library describes the protein topology on the super-secondary structure level. It allows to compute and visualize protein ligand graphs and search for protein structural motifs. The new server features additional information on ligand binding to secondary structure elements, increased usability and an application programming interface (API) to retrieve data, allowing for an automated analysis of protein topology. AVAILABILITY AND IMPLEMENTATION: The Protein Topology Graph Library server is freely available on the web at http://ptgl.uni-frankfurt.de. The website is implemented in PHP, JavaScript, PostgreSQL and Apache. It is supported by all major browsers. The VPLG software that was used to compute the protein ligand graphs and all other data in the database is available under the GNU public license 2.0 from http://vplg.sourceforge.net. CONTACT: tim.schaefer@bioinformatik.uni-frankfurt.de; ina.koch@bioinformatik.uni-frankfurt.de SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A Hybrid AI-Based Method for ICD Classification of Medical Documents.

Automatic document classification is a common problem that has successfully been addressed with machine learning methods. However, these methods require extensive training data, which is not always readily available. Additionally, in privacy-sensitive settings, transfer and reuse of trained machine learning models is not an option because sensitive information could potentially be reconstructed from the model. Therefore, we propose a transfer learning method that uses ontologies to normalize the feature space of text classifiers to create a controlled vocabulary. This ensures that the trained models do not contain personal data, and can be widely reused without violating the GDPR. Furthermore, the ontologies can be enriched so that the classifiers can be transferred to contexts with different terminology without additional training. Applying classifiers trained on medical documents to medical texts written in colloquial language shows promising results and highlights the potential of the approach. The compliance with GDPR by design opens many further application domains for transfer learning based solutions.