Martin Schmitz

This paper introduces grapHiC, a contrastive learning framework for reference-free haplotype phasing in de novo genome assembly, achieving results comparable to state-of-the-art methods.

DipGNNome introduces a deep learning framework for diploid genome assembly using graph neural networks and beam-search, achieving results comparable to state-of-the-art methods.

This paper introduces GNNome, a geometric deep learning framework for de novo genome assembly that improves contiguity compared to existing tools across multiple species.