Context:
Networks have long been popular means for modeling complex biological systems where they model interactions between biological entities and their effects on various biological systems. They also provide support for relational machine learning models which are known to provide highly scalable and accurate predictions of associations between biomedical entities, such as drugs, proteins or diseases.

Efficient implementation of computational predictive models that use various biomedical networks requires their integration and thorough analysis. However, readily-applicable means for such integration and analysis are rather scarce at the moment, which is a gap this work will address.

Goals:
- Study a selection of open biomedical networks relevant to the research project AIcope that can benefit from the results of this work. The most relevant datasets are Drugbank and SIDER, but any other dataset mentioned in the preliminary topic description (i.e., InterPro, ATC, CTD, KEGG, NCI Thesaurus or MeSH) may be incorporated as well.
- Suggest and implement a simple method for semantic integration of entities in the selected networks. The approach implemented in the BioKG project (https://github.com/dsi-bdi/biokg) may be a useful starting point here.
- Use the Vue.js framework to implement a prototype widget for visual exploration of biomedical networks, focusing primarily on the integrated selection of datasets.
- Write up the results in a thesis form.

Requirements:
- Keen interest in the topic.
- Ability to read and comprehend scientific publications and software documentation in English.
- Basic knowledge of computational biology, bioinformatics or medicine is a plus, but certainly not a must - the interest and willingness to learn are the only essential requirements, really.
- While the thesis can be written and defended in Czech, its elaboration and presentation in English will be supported enthusiastically.
- Monthly (or more frequent, if needed) progress review meetings with the supervisor will be expected.
- The student(s) will also be expected to develop and document any related code using the FI MU Gitlab platform, and (if applicable) re-use and interact with other related projects there.

Notes:
- This topic can be picked by more than one student, however, their works must be complementary in terms of the implemented methods and use cases covered.
- Should they be interested, the student may tap into the resources and informal feedback of a broader international group of biomedical AI researchers and practitioners based in the Czech Republic, Ireland and elsewhere.