Biomedical Network Science Lab
Welcome to the Biomedical Network Science Lab!
The Biomedical Network Science (BIONETS) lab investigates molecular disease mechanisms using techniques from network science, combinatorial optimization, and artificial intelligence. We develop algorithms and tools to mine multi-omics data for such mechanisms and to individuate novel strategies for mechanistically grounded drug repurposing and causally effective treatments of complex diseases. We also develop privacy-preserving decentralized biomedical AI solutions, which enable cross-institutional studies on sensitive data. Finally, we are interested in meta-scientific questions such as reproducibility and the impact of data bias on biomedical AI systems.
Our online tool for in silico validation of systems medicine hypotheses has just been published in Briefings in Bioinformatics.
We are happy to announce that our paper "Mining Temporal Anomalies in Healthcare Information Systems and Beyond" has been accepted at ADBIS 2022.
We are happy to announce that our paper on how to use enumeration of diverse minimum-cost perfect and error-correcting bipartite matchings for robust data matching has been published in Information Sciences.
Our ICDM 2021 paper on privacy-preserving federated principle component analysis for genome-wide association studies is now available in IEEE Xplore.
Our review paper on privacy-preserving AI techniques in biomedicine has been published in Methods of Information in Medicine.
