My main focus is the analysis of Neural Embedding/Language and Deep Learning Models applied in the field of pharmaceutical/medical digital libraries. Besides investigating the semantics that have been learned between pharmaceutical entities by these models, I investigate whether these models can be used not only to learn, but also to predict new relationships between pharmaceutical entities such as drug-disease associations.
Latest News
2021 Presentation at the 109. Bibliothekartag in Bremen
It was an honor for me to present our research and to talk about the successful collaboration with the University Library of Braunschweig.
2020/21 Presentation Videos
"Explainable Word-Embeddings for Medical Digital Libraries – a Context-Aware Approach", ACM/IEEE Joint Conference on Digital Libraries (JCDL), Xi'an, Shaanxi, China, 08/2020.
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"Mining Semantic Subspaces to Express Discipline-Specific Similarities", ACM/IEEE Joint Conference on Digital Libraries (JCDL), Xi'an, Shaanxi, China, 08/2020
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"Semantic Disambiguation of Embedded Drug-Disease Associations using Semantically Enriched Deep-Learning Approaches", Database Systems for Advanced Applications (DASFAA), Jeju, South Korea, Springer, 09/2020
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2020 Covid-19 and Sars-CoV-2 Web-Services
We re-trained our Neural-Network approaches with the actual biomedical literature (PubMed + WHO Database). As next we will also include the preprints from bioRxiv.org.
You can already search/explore the results for covid-19 and 50.000 other disease-related terms in PubPharm (Link). The facets “Related Substances”, “Related Symptoms/Diseases” and “Related Genes” show the relationships that were found/predicted by the Neural-Network.
In addition we also present the results for Sars-CoV-2 (Link).
2020 JCDL, DASFAA PC-Member/Chair
It is an honour to me to be a JCDL PC-Member as well as a DASFAA Chair this year.
2020 New PubPharm Prototype
The exponential growth of publications in the bio-medical field makes it increasingly difficult to access the information contained in literature. For example, tens of thousands of publications are published annually about the disease "diabetes". Such huge amounts of publications simply cannot be read by individuals anymore. In such a scenario, intelligent systems are needed that can automatically extract useful information for scientists from the literature.
One possible solution is the use of AI, which can automatically detect or even predict relationships between active substances, genes and diseases based on millions of publications. On the other hand, from a user’s perspective, it is often difficult to assess what an artificial neural network has learned explicitly. Based on our latest publications, we have developed a prototype that facilitates the exploration of learned and predicted drug-disease associations. In this context, network views provide a simple overview of the complex relationships between the different entities. Our prototype is currently being evaluated by the (pharmaceutical) community. The goal is to integrate it this summer.
2018 Innovative service for the pharmaceutical digital library PubPharm
Based on our work „Semantic Facettation in Pharmaceutical Collections using Deep Learning for Active Substance Contextualization“ we implemented an innovative service for the pharmaceutical digital library PubPharm. Our service provides an alternative access path to literature beyond mere keyword or bibliographic search.
2018 Presented innovative services on the CeBIT
We presented our research output on the community exhibitor stand of the Lower Saxony Ministry for Science and Culture.
We got also the chance to present our work to the secretary of state Dr. Sabine Johannsen on the largest and most
internationally representative computer expo (CeBIT).
2017 "Best Paper Award" awarded at 19th International Conference on Asia-Pacific Digital Libraries (ICADL’17), Bangkok, Thailand
Supervised Thesis
| Thesis Type |
Student Name |
Title |
| Master Thesis |
Vidya Mohan Sathya |
Drug-Repurposing by Exploring the Semantic Similarity of Drugs |
| Project Thesis |
Philipp Markiewka |
Topic Modeling and Topic Labeling of Deep-Learned Facets |
|
Research Project
|
Vidya Mohan Sathya
|
Semantic Context of Drugs using Neural Networking Mode
|
Lectures
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