Research Areas
We develop computational methods for systems and network medicine applications. Problems we are working on include inference of gene regulatory networks, mining of pathomechanisms in protein-protein interaction networks, dimensionality reduction for high-dimensional molecular data, and detection of differentially abundant cell populations in single-cell data.
Key Publications
Robust disease module mining via enumeration of diverse prize-collecting Steiner trees
In: Bioinformatics 38 (2022), p. 1600-1606
ISSN: 1367-4803
DOI: 10.1093/bioinformatics/btab876
URL: https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btab876/6497106?guestAccessKey=27c2a19b-11b5-4c3c-88d5-f1131e3809a9
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Network medicine for disease module identification and drug repurposing with the NeDRex platform
In: Nature Communications 12 (2021), Article No.: 6848
ISSN: 2041-1723
DOI: 10.1038/s41467-021-27138-2
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Exploring the SARS-CoV-2 virus-host-drug interactome for drug repurposing
In: Nature Communications 11 (2020), Article No.: 3518
ISSN: 2041-1723
DOI: 10.1038/s41467-020-17189-2
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We cary out research meta-scientific topics in computational biomedicine. Such topics include (lack of) reproducibility of biomedical AI systems, the effect of data leakage on such systems, and the impact of various forms of biases on data-driven methods in network and systems biology.
Key Publications
Lacking mechanistic disease definitions and corresponding association data hamper progress in network medicine and beyond
In: Nature Communications 14 (2023), Article No.: 1662
ISSN: 2041-1723
DOI: 10.1038/s41467-023-37349-4
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The AIMe registry for artificial intelligence in biomedical research
In: Nature Methods 18 (2021), p. 1128 - 1131
ISSN: 1548-7091
DOI: 10.1038/s41592-021-01241-0
URL: https://rdcu.be/cv5H7
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On the limits of active module identification
In: Briefings in Bioinformatics 22 (2021), Article No.: bbab066
ISSN: 1467-5463
DOI: 10.1093/bib/bbab066
URL: https://academic.oup.com/bib/advance-article/doi/10.1093/bib/bbab066/6189770?guestAccessKey=1be406a0-6b26-4e15-b32b-505c1ea2b279
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We develop privacy-aware methods for problems in computational biomedicine, mainly making use of federated learning. Problems we are working on include federated genome-wide association studies and privacy-aware computation of differentially expressed genes from distributed gene expression data.
Key Publications
Flimma: a federated and privacy-aware tool for differential gene expression analysis
In: Genome Biology 22 (2021), Article No.: 338
ISSN: 1474-760X
DOI: 10.1186/s13059-021-02553-2
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On the Privacy of Federated Pipelines
The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '21) (Virtual Event, 11/07/2021 - 15/07/2021)
In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '21), New York, NY, USA: 2021
DOI: 10.1145/3404835.3462996
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We develop graph-based algorithms for applied combinatorial optimization problems. Problems we are working on include the computation of distances between graphs and trees and enumeration of matchings and paths under diversity constraints.
Key Publications
The edge-preservation similarity for comparing rooted, unordered, node-labeled trees
In: Pattern Recognition Letters 167 (2023), p. 189-195
ISSN: 0167-8655
DOI: 10.1016/j.patrec.2023.02.017
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Enumerating dissimilar minimum cost perfect and error-correcting bipartite matchings for robust data matching
In: Information Sciences 596 (2022), p. 202-221
ISSN: 0020-0255
DOI: 10.1016/j.ins.2022.03.017
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Scalable generalized median graph estimation and its manifold use in bioinformatics, clustering, classification, and indexing
In: Information Systems 100 (2021), Article No.: 101766
ISSN: 0306-4379
DOI: 10.1016/j.is.2021.101766
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