ArticleProceedings of machine learning research2024
Position: Topological Deep Learning is the New Frontier for Relational Learning.
Article in Proceedings of machine learning research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 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
18 citing papers in PubMed.
- Article
- 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
- Manifold topological deep learning for biomedical data.Nature communications · 2026Article
- Commutative Algebra Modeling in Materials Science - A Case Study on Metal-Organic Frameworks (MOFs).Journal of chemical information and modeling · 2026Article
- Persistent Stanley-Reisner Theory.Foundations of data science (Springfield, Mo.) · 2026Article
- Computational Drug Repurposing for Alzheimer's Disease via Sheaf Theoretic Population-Scale Analysis of snRNA-Seq Data.Journal of medicinal chemistry · 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
- A Review of Topological Data Analysis and Topological Deep Learning in Molecular Sciences.Journal of chemical information and modeling · 2025Review
- Topological machine learning for protein-nucleic acid binding affinity changes upon mutation.Machine learning: science and technology · 2025Article
- CAP: Commutative algebra prediction of protein-nucleic acid binding affinities.Machine learning: science and technology · 2025Article
- CAML: Commutative Algebra Machine Learning─A Case Study on Protein-Ligand Binding Affinity Prediction.Journal of chemical information and modeling · 2025Article
- Manifold Topological Deep Learning for Biomedical Data.Research square · 2025Article
- TopoQA: a topological deep learning-based approach for protein complex structure interface quality assessment.Briefings in bioinformatics · 2025Article
- Persistent Topological Laplacians-A Survey.Mathematics (Basel, Switzerland) · 2025Article
- Persistent Mayer Dirac.Journal of physics. Complexity · 2024Article
- Persistent de Rham-Hodge Laplacians in Eulerian representation for manifold topological learning.AIMS mathematics · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
22 authors.
Funding
Abstract
Topological deep learning (TDL) is a rapidly evolving field that uses topological features to understand and design deep learning models. This paper posits that TDL is the new frontier for relational learning. TDL may complement graph representation learning and geometric deep learning by incorporating topological concepts, and can thus provide a natural choice for various machine learning settings. To this end, this paper discusses open problems in TDL, ranging from practical benefits to theoretical foundations. For each problem, it outlines potential solutions and future research opportunities. At the same time, this paper serves as an invitation to the scientific community to actively participate in TDL research to unlock the potential of this emerging field.
Identifiers
40196046PMC11973457What 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.