ArticleStatistics in medicine2018
Identifying gene-gene interactions using penalized tensor regression.
Article in Statistics in medicine, 2018. 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.
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
13 citing papers in PubMed.
- Multi-omics approaches for understanding gene-environment interactions in noncommunicable diseases: techniques, translation, and equity issues.Human genomics · 2025Review
- Searching for gene-gene interactions through variance quantitative trait loci of 29 continuous Taiwan Biobank phenotypes.Frontiers in genetics · 2024Article
- Two-level Bayesian interaction analysis for survival data incorporating pathway information.Biometrics · 2023Article
- Review
- Unified model-free interaction screening via CV-entropy filter.Computational statistics & data analysis · 2023Article
- Gene-environment interaction analysis via deep learning.Genetic epidemiology · 2023Article
- A General Framework for Identifying Hierarchical Interactions and Its Application to Genomics Data.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2023Article
- Gene-gene interaction analysis incorporating network information via a structured Bayesian approach.Statistics in medicine · 2021Article
- Genetic interactions effects for cancer disease identification using computational models: a review.Medical & biological engineering & computing · 2021Review
- Melanoma susceptibility: an update on genetic and epigenetic findings.International journal of molecular epidemiology and genetics · 2021Review
- Penalized integrative semiparametric interaction analysis for multiple genetic datasets.Statistics in medicine · 2019Article
- Robust genetic interaction analysis.Briefings in bioinformatics · 2019Review
- Identification of genes associated with cancer progression and prognosis in lung adenocarcinoma: Analyses based on microarray from Oncomine and The Cancer Genome Atlas databases.Molecular genetics & genomic medicine · 2019Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Gene-gene (G×G) interactions have been shown to be critical for the fundamental mechanisms and development of complex diseases beyond main genetic effects. The commonly adopted marginal analysis is limited by considering only a small number of G factors at a time. With the "main effects, interactions" hierarchical constraint, many of the existing joint analysis methods suffer from prohibitively high computational cost. In this study, we propose a new method for identifying important G×G interactions under joint modeling. The proposed method adopts tensor regression to accommodate high data dimensionality and the penalization technique for selection. It naturally accommodates the strong hierarchical structure without imposing additional constraints, making optimization much simpler and faster than in the existing studies. It outperforms multiple alternatives in simulation. The analysis of The Cancer Genome Atlas (TCGA) data on lung cancer and melanoma demonstrates that it can identify markers with important implications and better prediction performance.
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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.