ArticleGenetic epidemiology2014
Integrative analysis of high-throughput cancer studies with contrasted penalization.
Article in Genetic epidemiology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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
14 citing papers in PubMed.
- Integrative rank-based regression for multi-source high-dimensional data with multi-type responses.Journal of applied statistics · 2025Article
- Prior information-assisted integrative analysis of multiple datasets.Bioinformatics (Oxford, England) · 2023Article
- Modeling Pregnancy Outcomes through Sequentially Nested Regression Models.Journal of the American Statistical Association · 2022Article
- Integrative sparse partial least squares.Statistics in medicine · 2021Article
- An integrative sparse boosting analysis of cancer genomic commonality and difference.Statistical methods in medical research · 2020Article
- Integrating approximate single factor graphical models.Statistics in medicine · 2020Article
- Identification of cancer omics commonality and difference via community fusion.Statistics in medicine · 2019Article
- Robust network-based analysis of the associations between (epi)genetic measurements.Journal of multivariate analysis · 2018Article
- Clustering multilayer omics data using MuNCut.BMC genomics · 2018Article
- Analysis of cancer gene expression data with an assisted robust marker identification approach.Genetic epidemiology · 2017Article
- Integrative sparse principal component analysis of gene expression data.Genetic epidemiology · 2017Article
- Promoting Similarity of Sparsity Structures in Integrative Analysis with Penalization.Journal of the American Statistical Association · 2017Article
- Article
- Integrative Interaction Analysis using Threshold Gradient Directed Regularization.Applied stochastic models in business and industryArticle
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.
Funding
Abstract
In cancer studies with high-throughput genetic and genomic measurements, integrative analysis provides a way to effectively pool and analyze heterogeneous raw data from multiple independent studies and outperforms "classic" meta-analysis and single-dataset analysis. When marker selection is of interest, the genetic basis of multiple datasets can be described using the homogeneity model or the heterogeneity model. In this study, we consider marker selection under the heterogeneity model, which includes the homogeneity model as a special case and can be more flexible. Penalization methods have been developed in the literature for marker selection. This study advances from the published ones by introducing the contrast penalties, which can accommodate the within- and across-dataset structures of covariates/regression coefficients and, by doing so, further improve marker selection performance. Specifically, we develop a penalization method that accommodates the across-dataset structures by smoothing over regression coefficients. An effective iterative algorithm, which calls an inner coordinate descent iteration, is developed. Simulation shows that the proposed method outperforms the benchmark with more accurate marker identification. The analysis of breast cancer and lung cancer prognosis studies with gene expression measurements shows that the proposed method identifies genes different from those using the benchmark and has better prediction performance.
Indexed as
Identifiers
What 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.