ArticleStatistics in medicine2019
Identification of cancer omics commonality and difference via community fusion.
Article in Statistics in medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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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Who cites it
4 citing papers in PubMed.
- Biomarker-guided heterogeneity analysis of genetic regulations via multivariate sparse fusion.Statistics in medicine · 2021Article
- 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
- Penalized integrative semiparametric interaction analysis for multiple genetic datasets.Statistics in medicine · 2019Article
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Authors and funding
4 authors.
Funding
Abstract
The analysis of cancer omics data is a "classic" problem; however, it still remains challenging. Advancing from early studies that are mostly focused on a single type of cancer, some recent studies have analyzed data on multiple "related" cancer types/subtypes, examined their commonality and difference, and led to insightful findings. In this article, we consider the analysis of multiple omics datasets, with each dataset on one type/subtype of "related" cancers. A Community Fusion (CoFu) approach is developed, which conducts marker selection and model building using a novel penalization technique, informatively accommodates the network community structure of omics measurements, and automatically identifies the commonality and difference of cancer omics markers. Simulation demonstrates its superiority over direct competitors. The analysis of TCGA lung cancer and melanoma data leads to interesting findings.
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