Daniel Bruneß

Daniel Bruneß

M.Sc. Bioinformatik | Ph.D. Student Computer Science @ Goethe Universität

Posts by Collection

portfolio

publications

The new protein topology graph library web server

Published in Bioinformatics, 2016

A new, extended version of the Protein Topology Graph Library web server is presented, featuring 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.

Recommended citation: Schäfer, T., Scheck, A., Bruneß, D., May, P., & Koch, I. (2016). The new protein topology graph library web server. Bioinformatics, 32 3, 474-6 . https://doi.org/10.1093/bioinformatics%2Fbtv574

Interacting Spider Webs

Published in The Art of Theoretical Biology, 2020

The bacterial pathogen Salmonella Typhimurium provokes gastroenteritis and typhoid fever. Salmonella become multidrug resistant.

Recommended citation: Rieser, J., Bruneß, D., Ackermann, J., Koch, I. (2020). Interacting Spider Webs. In: Matthäus, F., Matthäus, S., Harris, S., Hillen, T. (eds) The Art of Theoretical Biology. Springer https://doi.org/10.1007/978-3-030-33471-0_68

Term Extraction from Medical Documents Using Word Embeddings

Published in 2020 6th IEEE Congress on Information Science and Technology (CiSt), 2021

A new method for the extraction of discipline-specific terms from medical documents using word embeddings in a neighborhood context based method which is called Snowball because of its layerwise way of working.

Recommended citation: Bay, M., Bruneß, D., Herold, M., Schulze, C., Guckert, M., & Minor, M. (2020). Term Extraction from Medical Documents Using Word Embeddings. 2020 6th IEEE Congress on Information Science and Technology (CiSt), 328-333. https://doi.org/10.1109/CiSt49399.2021.9357263

An Ontology-based transfer learning method improving classification of medical documents.

Published in 2020 6th IEEE Congress on Information Science and Technology (CiSt), 2022

A transfer learning method which uses ontologies to normalise the feature space of text classifiers to guarantee that the trained models do not contain any person related data and can therefore be widely reused without raising General Data Protection Regulation issues.

Recommended citation: Bruneß, D., Bay, M., Schulze, C., Guckert, M., & Minor, M. (2022). An Ontology-based transfer learning method improving classification of medical documents. 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA), 407-412. https://doi.org/10.1109/ICMLA55696.2022.00065

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

Published in 2020 6th IEEE Congress on Information Science and Technology (CiSt), 2022

A transfer learning method that uses ontologies to normalize the feature space of text classifiers to create a controlled vocabulary that ensures that the trained models do not contain personal data, and can be widely reused without violating the GDPR.

Recommended citation: Bruneß, D., Bay, M., Schulze, C., Guckert, M., & Minor, M. (2023). A Hybrid AI-Based Method for ICD Classification of Medical Documents. Healthcare Transformation with Informatics and Artificial Intelligence, 305, 1-4.. https://doi.org/10.3233/SHTI230408

talks

teaching

Programming 1

Tutorial, Goethe University, Computer Science, 2013

(dt. Programmierung 1)

Mobile Technologies

Seminar, Technische Hochschule Mittelhessen, StudiumPlus, 2020

(dt. Mobile Technologien)