ArticleIEEE transactions on pattern analysis and machine intelligence2017
Principal Graph and Structure Learning Based on Reversed Graph Embedding.
Article in IEEE transactions on pattern analysis and machine intelligence, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
What it found
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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.
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Who cites it
29 citing papers in PubMed.
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- Revisiting Abnormalities of Ventricular Depolarization: Redefining Phenotypes and Associated Outcomes Using Tree-Based Dimensionality Reduction.Journal of the American Heart Association · 2025Article
- Trajectory Inference for Single Cell Omics.ArXiv · 2025Article
- FLASC: a flare-sensitive clustering algorithm.PeerJ. Computer science · 2025Article
- TrajVis: a visual clinical decision support system to translate artificial intelligence trajectory models in the precision management of chronic kidney disease.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- scCRT: a contrastive-based dimensionality reduction model for scRNA-seq trajectory inference.Briefings in bioinformatics · 2024Article
- Unraveling temporal and spatial biomarkers of epithelial-mesenchymal transition in colorectal cancer: insights into the crucial role of immunosuppressive cells.Journal of translational medicine · 2023Article
- Review
- Inferring evolutionary trajectories from cross-sectional transcriptomic data to mirror lung adenocarcinoma progression.PLoS computational biology · 2023Article
- Multiscale integration of human and single-cell variations reveals unadjuvanted vaccine high responders are naturally adjuvanted.medRxiv : the preprint server for health sciences · 2023Article
- RobustTree: An adaptive, robust PCA algorithm for embedded tree structure recovery from single-cell sequencing data.Frontiers in genetics · 2023Article
- Identification of tumor antigens and immune landscapes for bladder urothelial carcinoma mRNA vaccine.Frontiers in immunology · 2023Article
- Heterogeneity in phenotype, disease progression and drug response in type 2 diabetes.Nature medicine · 2022Article
- Identification of Neoantigens and Construction of Immune Subtypes in Prostate Adenocarcinoma.Frontiers in genetics · 2022Article
- Article
- A single-cell view of the transcriptome during lateral root initiation in Arabidopsis thaliana.The Plant cell · 2021Article
- Compensatory hepatic adaptation accompanies permanent absence of intrahepatic biliary network due to YAP1 loss in liver progenitors.Cell reports · 2021Article
- Optimal Estimation of Wasserstein Distance on A Tree with An Application to Microbiome Studies.Journal of the American Statistical Association · 2021Article
- Identification of Tumor Antigens and Design of mRNA Vaccine for Colorectal Cancer Based on the Immune Subtype.Frontiers in cell and developmental biology · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Many scientific datasets are of high dimension, and the analysis usually requires retaining the most important structures of data. Principal curve is a widely used approach for this purpose. However, many existing methods work only for data with structures that are mathematically formulated by curves, which is quite restrictive for real applications. A few methods can overcome the above problem, but they either require complicated human-made rules for a specific task with lack of adaption flexibility to different tasks, or cannot obtain explicit structures of data. To address these issues, we develop a novel principal graph and structure learning framework that captures the local information of the underlying graph structure based on reversed graph embedding. As showcases, models that can learn a spanning tree or a weighted undirected `1 graph are proposed, and a new learning algorithm is developed that learns a set of principal points and a graph structure from data, simultaneously. The new algorithm is simple with guaranteed convergence. We then extend the proposed framework to deal with large-scale data. Experimental results on various synthetic and six real world datasets show that the proposed method compares favorably with baselines and can uncover the underlying structure correctly.
Identifiers
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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.