ArticleThe annals of applied statistics2017
A STATISTICAL FRAMEWORK FOR DATA INTEGRATION THROUGH GRAPHICAL MODELS WITH APPLICATION TO CANCER GENOMICS.
Article in The annals of applied statistics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- Integrative Analysis of Multimodal Omics Data.Annual review of statistics and its application · 2026Article
- A fast integrative clustering and feature selection approach for high-dimensional multiview data.Statistical methods in medical research · 2026Article
- NJGCG: A node-based joint Gaussian copula graphical model for gene networks inference across multiple states.Computational and structural biotechnology journal · 2024Article
- A new graph-based clustering method with application to single-cell RNA-seq data from human pancreatic islets.NAR genomics and bioinformatics · 2021Article
- Integration of Metabolomic and Other Omics Data in Population-Based Study Designs: An Epidemiological Perspective.Metabolites · 2019Review
- Microbiome Multi-Omics Network Analysis: Statistical Considerations, Limitations, and Opportunities.Frontiers in genetics · 2019Review
- A STATISTICAL FRAMEWORK FOR DATA INTEGRATION THROUGH GRAPHICAL MODELS WITH APPLICATION TO CANCER GENOMICS.The annals of applied statistics · 2017Article
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
Recent advances in high-throughput biotechnologies have generated var-ious types of genetic, genomic, epigenetic, transcriptomic and proteomic data across different biological conditions. It is likely that integrating data from diverse experiments may lead to a more unified and global view of biolog-ical systems and complex diseases. We present a coherent statistical frame-work for integrating various types of data from distinct but related biological conditions through graphical models. Specifically, our statistical framework is designed for modeling multiple networks with shared regulatory mech-anisms from heterogeneous high-dimensional datasets. The performance of our approach is illustrated through simulations and its applications to cancer genomics.
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.