ArticleFoundations of data science (Springfield, Mo.)2021
HERMES: PERSISTENT SPECTRAL GRAPH SOFTWARE.
Article in Foundations of data science (Springfield, Mo.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
28 citing papers in PubMed.
- Persistent sheaf Laplacian analysis of protein stability and solubility changes upon mutation.Protein science : a publication of the Protein Society · 2026Article
- Correlated clustering and projection for dimensionality reduction.Machine learning: science and technology · 2026Article
- Topological data analysis and topological deep learning beyond persistent homology: a review.Artificial intelligence review · 2026Article
- Interpretability and Representability of Commutative Algebra, Algebraic Topology, and Topological Spectral Theory for Real-World Data.Advanced intelligent discovery · 2025Article
- Topological deep learning for enhancing peptide-protein complex prediction.Communications chemistry · 2025Article
- PERSISTENT DIRECTED FLAG LAPLACIAN.Foundations of data science (Springfield, Mo.) · 2025Article
- How large is the universe of RNA-like motifs? A clustering analysis of RNA graph motifs using topological descriptors.PLoS computational biology · 2025Article
- PERSISTENT SHEAF LAPLACIANS.Foundations of data science (Springfield, Mo.) · 2025Article
- Persistent Topological Laplacians-A Survey.Mathematics (Basel, Switzerland) · 2025Article
- Topology-preserving Hodge decomposition in the Eulerian representation.Beijing journal of pure & applied mathematics · 2025Article
- Analyzing Single Cell RNA Sequencing with Topological Nonnegative Matrix Factorization.Journal of computational and applied mathematics · 2024Article
- PERSISTENT DIRAC OF PATHS ON DIGRAPHS AND HYPERGRAPHS.Foundations of data science (Springfield, Mo.) · 2024Article
- K-nearest-neighbors induced topological PCA for single cell RNA-sequence data analysis.Computers in biology and medicine · 2024Article
- PLPCA: Persistent Laplacian-Enhanced PCA for Microarray Data Analysis.Journal of chemical information and modeling · 2024Article
- Persistent de Rham-Hodge Laplacians in Eulerian representation for manifold topological learning.AIMS mathematics · 2024Article
- PERSISTENT HYPERDIGRAPH HOMOLOGY AND PERSISTENT HYPERDIGRAPH LAPLACIANS.Foundations of data science (Springfield, Mo.) · 2023Article
- Article
- Persistent topological Laplacian analysis of SARS-CoV-2 variants.Journal of computational biophysics and chemistry · 2023Article
- Persistent Dirac for molecular representation.Scientific reports · 2023Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Persistent homology (PH) is one of the most popular tools in topological data analysis (TDA), while graph theory has had a significant impact on data science. Our earlier work introduced the persistent spectral graph (PSG) theory as a unified multiscale paradigm to encompass TDA and geometric analysis. In PSG theory, families of persistent Laplacian matrices (PLMs) corresponding to various topological dimensions are constructed via a filtration to sample a given dataset at multiple scales. The harmonic spectra from the null spaces of PLMs offer the same topological invariants, namely persistent Betti numbers, at various dimensions as those provided by PH, while the non-harmonic spectra of PLMs give rise to additional geometric analysis of the shape of the data. In this work, we develop an open-source software package, called highly efficient robust multidimensional evolutionary spectra (HERMES), to enable broad applications of PSGs in science, engineering, and technology. To ensure the reliability and robustness of HERMES, we have validated the software with simple geometric shapes and complex datasets from three-dimensional (3D) protein structures. We found that the smallest non-zero eigenvalues are very sensitive to data abnormality.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.