ArticleProceedings of the National Academy of Sciences of the United States of America2014
Learning regulatory programs by threshold SVD regression.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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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
8 citing papers in PubMed.
- Sparse Single Index Models for Multivariate Responses.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2021Article
- Multi-omics Data Integration, Interpretation, and Its Application.Bioinformatics and biology insights · 2020Review
- SOFAR: Large-Scale Association Network Learning.IEEE transactions on information theory · 2019Article
- Modeling the causal regulatory network by integrating chromatin accessibility and transcriptome data.National science review · 2016Article
- Model diagnostics in reduced-rank estimation.Statistics and its interface · 2016Article
- Inference of transcriptional regulation in cancers.Proceedings of the National Academy of Sciences of the United States of America · 2015Article
- Integrative analyses of cancer data: a review from a statistical perspective.Cancer informatics · 2015Review
- A note on rank reduction in sparse multivariate regression.Journal of statistical theory and practiceArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
We formulate a statistical model for the regulation of global gene expression by multiple regulatory programs and propose a thresholding singular value decomposition (T-SVD) regression method for learning such a model from data. Extensive simulations demonstrate that this method offers improved computational speed and higher sensitivity and specificity over competing approaches. The method is used to analyze microRNA (miRNA) and long noncoding RNA (lncRNA) data from The Cancer Genome Atlas (TCGA) consortium. The analysis yields previously unidentified insights into the combinatorial regulation of gene expression by noncoding RNAs, as well as findings that are supported by evidence from the literature.
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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.