NetMap is presented, a pipeline that jointly infers cell-level GRNs and clusters cells based on shared regulatory programs, combining state-of-the-art single-cell autoencoders and explainable artificial intelligence methods that outperforms existing methods on synthetic data with a known ground-truth GRN and is able to...
A. Hartebrodt, Mhaned Oubounyt, G. D. de Almeida et al.· bioRxiv· 0 citations
Abstract Alzheimer’s disease is a complex neurodegenerative disorder characterized by progressive cognitive decline and neuroinflammation. Although its molecular hallmarks are well documented, cell-type-specific mechanisms driving gene dysregulation remain elusive. While single-cell RNA sequencing resolves cellular sta...
Andrea Álvarez-Pérez, Alejandro Rodríguez González, Jan Baumbach et al.· NAR Genomics and Bioinformat...· 0 citations
Protein-protein interaction (PPI) network alignment combines topological and sequence information to identify conserved modules across species, but global alignment remains challenging: heuristics sacrifice optimality, while exact methods lack scalability. We model the alignment as a weighted maximum common induced sub...
M. Stahl, Robert J. Banks, M. Traube et al.· 0 citations
Foundation models have demonstrated immense value for scRNA-seq analysis, but their fine-tuning or inference on heterogeneous, privacy-sensitive clinical cohorts is governed by strict data protection policies, which often prohibit centralization. We introduce Clifti-GPT, a privacy-preserving federated framework based o...
Mohammad Bakhtiari, M. Elkjaer, Ali Oğuz Can et al.· BioData Mining· 0 citations
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