Evidence map›Paper›PMID 33224404›Full record

ArticleThe annals of applied statistics2019

RADIO-IBAG: RADIOMICS-BASED INTEGRATIVE BAYESIAN ANALYSIS OF MULTIPLATFORM GENOMIC DATA.

Youyi Zhang, Jeffrey S Morris, Shivali Narang Aerry, Arvind U K Rao, Veerabhadran Baladandayuthapani

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in The annals of applied statistics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Youyi ZhangThe University of Texas MD Anderson Cancer Center.
Jeffrey S MorrisThe University of Texas MD Anderson Cancer Center.
Shivali Narang AerryJohns Hopkins University.
Arvind U K RaoUniversity of Michigan Ann Arbor.
Veerabhadran BaladandayuthapaniUniversity of Michigan Ann Arbor.

Funding

Tumor Evolution and Metastasis ProgramP30CA016672 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI Fabien Delerue · 1985 to 2026
$290.8M
Bayesian methods for complex, high-dimensional functional data in cancer researchR01CA178744 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI MORRIS, JEFFREY S · 2015 to 2019
$1.7M
Integrative methods for high-dimensional genomics dataR01CA160736 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI BALADANDAYUTHAPANI, VEERABHADRAN · 2011 to 2014
$1.4M
NCI NIH HHS P30 CA016672NCI NIH HHS R01 CA160736NCI NIH HHS R01 CA178744
6 · The paper itself

Abstract

Technological innovations have produced large multi-modal datasets that include imaging and multi-platform genomics data. Integrative analyses of such data have the potential to reveal important biological and clinical insights into complex diseases like cancer. In this paper, we present Bayesian approaches for integrative analysis of radiological imaging and multi-platform genomic data, wherein our goals are to simultaneously identify genomic and radiomic, i.e., radiology-based imaging markers, along with the latent associations between these two modalities, and to detect the overall prognostic relevance of the combined markers. For this task, we propose

Identifiers

PMID33224404
PMCPMC7678720

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Registered trials

None linked

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.