Newest Advances on the FeatureCloud Platform for Federated Learning in Biomedicine

AI in biomedicine has been a central research topic in recent years. Although there are many different techniques and strategies, the majority rely on data that is of both high quality and quantity. Despite the steady growth in the amount of data generated for patients, it is frequently difficult to make that data useful for research because of strong restrictions through privacy regulations such as the GDPR. Through federated learning (FL), we are able to use distributed data for machine learning while keeping patient data inside the respective hospital. Instead of sharing the patient data, like in traditional machine learning, each participant trains an individual machine learning model and shares the model parameters and weights. Existing FL frameworks, however, frequently have restrictions on certain algorithms or application domains, and they frequently call for programming knowledge. With FeatureCloud, we addressed these limitations and provided a user-friendly solution for both developers and end-users. FeatureCloud greatly simplifies the complexity of developing federated applications and executing FL analyses in multi-institutional settings. Additionally, it provides an app store that makes it easy for the community to publish and reuse federated algorithms. Apps can be chained together to form pipelines and executed without programming knowledge, making them ideal for flexible clinical applications. Apps on FeatureCloud can receive certification from both internal and external reviewers to guarantee safety. FeatureCloud effectively separates local components from sensitive data systems by utilizing containerization technology, making it robust to execute in any system environment and guaranteeing data security. To further ensure the privacy of data, FeatureCloud incorporates privacy-enhancing technologies and complies with strict data privacy regulations, such as GDPR.Book of abstract: 4th Belgrade Bioinformatics Conference, June 19-23, 202

Democratizing knowledge representation with BioCypher

International audienceStandardising the representation of biomedical knowledge among allresearchers is an insurmountable task, hindering the effectiveness of manycomputational methods. To facilitate harmonisation and interoperability despitethis fundamental challenge, we propose to standardise the framework ofknowledge graph creation instead. We implement this standardisation inBioCypher, a FAIR (findable, accessible, interoperable, reusable) framework totransparently build biomedical knowledge graphs while preserving provenances ofthe source data. Mapping the knowledge onto biomedical ontologies helps tobalance the needs for harmonisation, human and machine readability, and ease ofuse and accessibility to non-specialist researchers. We demonstrate the usefulnessof the framework on a variety of use cases, from maintenance of task-specificknowledge stores, to interoperability between biomedical domains, to on-demandbuilding of task-specific knowledge graphs for federated learning. BioCypher(https://biocypher.org) thus facilitates automating knowledge-based biomedicalresearch, and we encourage the community to further develop and use it