Persistent de Rham-Hodge Laplacians in Eulerian representation for manifold topological learning is a research paper published in AIMS Mathematics (2024). On theSindex it has a DataRank of 0. It has been cited 8 times.
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NIAID NIH HHS
Grant: R01 AI164266
NIGMS NIH HHS
Grant: R01 GM126189
NIGMS NIH HHS
Grant: R35 GM148196
National Science Foundation
Grant: 2052983
Collaborative Research: Integrating Algebraic Topology, Graph Theory, and Multiscale Analysis for Learning Complex and Diverse Datasets
National Institutes of Health
Grant: 1R01AI164266-01A1
AI-based platform for predicting emerging vaccine-escape variants and designing mutation-proof antibodies
National Science Foundation
Grant: 1900473
III: Medium: De Rham-Hodge theory modeling and learning of biomolecular data
National Institutes of Health
Grant: 5R35GM148196-02
Discovery-Driven Mathematics and Artificial Intelligence for Biosciences and Drug Discovery
National Science Foundation
Grant: 1761320
Kinetics-Driven Drug Discovery Using Persistent Homology, Rare-Event Molecular Dynamics and Experimental Data
National Institutes of Health
Grant: 5R01GM126189-03
Synergistic integration of topology and machine learning for the predictions of protein-ligand binding affinities and mutation impacts
FWCI
2.31
Citation Percentile
0.9%
Citation Trend
Fields of Study
Keywords