110 citations · 174 across the 10 of their papers we have counts for
11 papers
Are You Learning Biological Signal or Shortcuts? Auditing and Mitigating Bias in Protein-Protein Interaction Datasets
Judith Bernett, Anton Spannagl, Joel Ås +2
Protein-protein interaction (PPI) databases do not faithfully reflect biological realities. Instead, they are influenced by study and technical biases that distort certain protein…
Federated singular value decomposition for high dimensional data
Anne Hartebrodt, Richard Röttger, David B. Blumenthal
Federated learning (FL) is emerging as a privacy-aware alternative to classical cloud-based machine learning. In FL, the sensitive data remains in data silos and only aggregated pa…
Metric Indexing for Graph Similarity Search
Franka Bause, David B. Blumenthal, Erich Schubert +1
Finding the graphs that are most similar to a query graph in a large database is a common task with various applications. A widely-used similarity measure is the graph edit distanc…
The Minimum Edit Arborescence Problem and Its Use in Compressing Graph Collections [Extended Version]
Lucas Gnecco, Nicolas Boria, Sébastien Bougleux +2
The inference of minimum spanning arborescences within a set of objects is a general problem which translates into numerous application-specific unsupervised learning tasks. We int…
Flimma: a federated and privacy-preserving tool for differential gene expression analysis
Olga Zolotareva, Reza Nasirigerdeh, Julian Matschinske +9
Aggregating transcriptomics data across hospitals can increase sensitivity and robustness of differential expression analyses, yielding deeper clinical insights. As data exchange i…
Federated Multi-Mini-Batch: An Efficient Training Approach to Federated Learning in Non-IID Environments
Reza Nasirigerdeh, Mohammad Bakhtiari, Reihaneh Torkzadehmahani +4
Federated learning has faced performance and network communication challenges, especially in the environments where the data is not independent and identically distributed (IID) ac…
Privacy-preserving Artificial Intelligence Techniques in Biomedicine
Reihaneh Torkzadehmahani, Reza Nasirigerdeh, David B. Blumenthal +22
Artificial intelligence (AI) has been successfully applied in numerous scientific domains. In biomedicine, AI has already shown tremendous potential, e.g. in the interpretation of…
New Techniques for Graph Edit Distance Computation
David B. Blumenthal
Due to their capacity to encode rich structural information, labeled graphs are often used for modeling various kinds of objects such as images, molecules, and chemical compounds.…
Improved local search for graph edit distance
Nicolas Boria, David B. Blumenthal, Sébastien Bougleux +1
The graph edit distance (GED) measures the dissimilarity between two graphs as the minimal cost of a sequence of elementary operations transforming one graph into another. This mea…
Upper Bounding the Graph Edit Distance Based on Rings and Machine Learning
David B. Blumenthal, Johann Gamper, Sébastien Bougleux +1
The graph edit distance (GED) is a flexible distance measure which is widely used for inexact graph matching. Since its exact computation is NP-hard, heuristics are used in practic…
Finding k-Dissimilar Paths with Minimum Collective Length
Theodoros Chondrogiannis, Panagiotis Bouros, Johann Gamper +2
Shortest path computation is a fundamental problem in road networks. However, in many real-world scenarios, determining solely the shortest path is not enough. In this paper, we st…