ArticleJournal of machine learning research : JMLR2014
Learning Graphical Models With Hubs.
Article in Journal of machine learning research : JMLR, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 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
23 citing papers in PubMed, 49 citations in OpenAlex.
- A modeling framework for detecting and leveraging node-level information in Bayesian network inference.Biostatistics (Oxford, England) · 2025Article
- Identifying High-Dimensional Genomic Factors Modulating Biological Networks Across MultiOmics Data.bioRxiv : the preprint server for biology · 2025Article
- tvsfglasso: Time-varying scale-free graphical lasso for network estimation from time-series data.PLoS computational biology · 2025Article
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- NJGCG: A node-based joint Gaussian copula graphical model for gene networks inference across multiple states.Computational and structural biotechnology journal · 2024Article
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- Fairness-Aware Estimation of Graphical Models.Advances in neural information processing systems · 2024Article
- Machine-learning methods based on the texture and non-texture features of MRI for the preoperative prediction of sentinel lymph node metastasis in breast cancer.Translational cancer research · 2023Article
- Predictive overfitting in immunological applications: Pitfalls and solutions.Human vaccines & immunotherapeutics · 2023Review
- SpiderLearner: An ensemble approach to Gaussian graphical model estimation.Statistics in medicine · 2023Article
- Gaussian graphical models with applications to omics analyses.Statistics in medicine · 2022Article
- Learning complex dependency structure of gene regulatory networks from high dimensional microarray data with Gaussian Bayesian networks.Scientific reports · 2022Article
- SPARSE MATRIX LINEAR MODELS FOR STRUCTURED HIGH-THROUGHPUT DATA.The annals of applied statistics · 2022Article
- Extended graphical lasso for multiple interaction networks for high dimensional omics data.PLoS computational biology · 2021Article
- Estimating and inferring the maximum degree of stimulus-locked time-varying brain connectivity networks.Biometrics · 2021Article
- Network analysis methods for studying microbial communities: A mini review.Computational and structural biotechnology journal · 2021Review
- Integrating approximate single factor graphical models.Statistics in medicine · 2020Article
- Identifying gene-environment interactions incorporating prior information.Statistics in medicine · 2019Article
- Bayesian inference of hub nodes across multiple networks.Biometrics · 2019Article
- Supervised learning via the "hubNet" procedure.Statistica Sinica · 2018Article
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 at 2 institutions in 1 country.
Funding
Abstract
We consider the problem of learning a high-dimensional graphical model in which there are a few
Indexed as
Identifiers
25620891PMC4302963W2106895029What 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.