ArticleJournal of the American Statistical Association2016
Estimation of Directed Acyclic Graphs Through Two-stage Adaptive Lasso for Gene Network Inference.
Article in Journal of the American Statistical Association, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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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.
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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
13 citing papers in PubMed.
- Learning directed acyclic graphs for ligands and receptors based on spatially resolved transcriptomic data of ovarian cancer.Briefings in bioinformatics · 2025Article
- SEMbap: Bow-free covariance search and data de-correlation.PLoS computational biology · 2024Article
- Molecular adaptations underlying high-frequency hearing in the brain of CF bats species.BMC genomics · 2024Article
- Inferred networks, machine learning, and health data.PloS one · 2023Article
- Consistent Second-Order Conic Integer Programming for Learning Bayesian Networks.Journal of machine learning research : JMLR · 2023Article
- DAGBagM: learning directed acyclic graphs of mixed variables with an application to identify protein biomarkers for treatment response in ovarian cancer.BMC bioinformatics · 2022Article
- An eco-evo-devo genetic network model of stress response.Horticulture research · 2022Article
- Review
- Integer Programming for Learning Directed Acyclic Graphs from Continuous Data.INFORMS journal on optimization · 2021Article
- Joint skeleton estimation of multiple directed acyclic graphs for heterogeneous population.Biometrics · 2019Article
- An omnidirectional visualization model of personalized gene regulatory networks.NPJ systems biology and applications · 2019Article
- Learning Subject-Specific Directed Acyclic Graphs With Mixed Effects Structural Equation Models From Observational Data.Frontiers in genetics · 2018Article
- Gene Regulatory Network Analysis for Triple-Negative Breast Neoplasms by Using Gene Expression Data.Journal of breast cancer · 2017Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Graphical models are a popular approach to find dependence and conditional independence relationships between gene expressions. Directed acyclic graphs (DAGs) are a special class of directed graphical models, where all the edges are directed edges and contain no directed cycles. The DAGs are well known models for discovering causal relationships between genes in gene regulatory networks. However, estimating DAGs without assuming known ordering is challenging due to high dimensionality, the acyclic constraints, and the presence of equivalence class from observational data. To overcome these challenges, we propose a two-stage adaptive Lasso approach, called NS-DIST, which performs neighborhood selection (NS) in stage 1, and then estimates DAGs by the Discrete Improving Search with Tabu (DIST) algorithm within the selected neighborhood. Simulation studies are presented to demonstrate the effectiveness of the method and its computational efficiency. Two real data examples are used to demonstrate the practical usage of our method for gene regulatory network inference.
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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.