Collapsed Effective Operators use Schur complement on graded Laplacians to create vertex-level operators that encode higher-order topology, preserve PSD, and improve spectral clustering and smoothing.
Topotein: Topological deep learning for protein representation learning.arXiv preprint arXiv:2509.03885
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STRAND treats persistence diagrams as survival data to derive a calibrated two-sample test, interpretable effect sizes, and a 1-Wasserstein-stable feature vector from one representation.
BioBlobs compresses proteins into a small set of cohesive substructures and predicts function from these blobs alone, recovering catalytic sites from protein-level labels across multiple encoders.
citing papers explorer
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Collapsed Effective Operators for Higher-order Structures
Collapsed Effective Operators use Schur complement on graded Laplacians to create vertex-level operators that encode higher-order topology, preserve PSD, and improve spectral clustering and smoothing.
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From Persistence to Survival: Hypothesis Testing, Effect Sizes and Vectorisation for Topological Features
STRAND treats persistence diagrams as survival data to derive a calibrated two-sample test, interpretable effect sizes, and a 1-Wasserstein-stable feature vector from one representation.
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BioBlobs: Unsupervised Discovery of Functional Substructures for Protein Function Prediction
BioBlobs compresses proteins into a small set of cohesive substructures and predicts function from these blobs alone, recovering catalytic sites from protein-level labels across multiple encoders.