Evidence map›Paper›PMID 30034293›Full record

ArticleStatistical modelling2017

Rejoinder to statistical contributions to bioinformatics: Design, modelling, structure learning and Integration.

Jeffrey S Morris, Veerabhadran Baladandayuthapani

Abstract read
In one paragraph

Article in Statistical modelling, 2017. 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. Article
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

2 authors.

Jeffrey S MorrisDepartment of Biostatistics, UT MD Anderson Cancer Center, Houston, Texas, USA.
Veerabhadran BaladandayuthapaniDepartment of Biostatistics, UT MD Anderson Cancer Center, Houston, Texas, USA.

Funding

Tumor Evolution and Metastasis ProgramP30CA016672 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI Scott Kopetz · 1985 to 2026
$290.8M
Graph-based Bayesian analysis of Genomics and Proteomics DataR01CA194391 · NCI · TEXAS A&M UNIVERSITY · PI BALADANDAYUTHAPANI, VEERABHADRAN, MALLICK, BANI K · 2016 to 2020
$2.0M
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 CA178744NCI NIH HHS R01 CA194391
6 · The paper itself

Abstract

We thank the discussants for their kind comments and their insightful analysis and discussion that has substantially added to the contribution of this issue. Overall, it seems the discussants have affirmed many of our primary points, and have also raised a number of other relevant and important issues that we did not emphasize in the paper. Several common threads emerged from these discussions, including the importance of software development, appropriate dissemination, and close collaboration with biomedical scientists and technology experts in order to ensure our work is relevant and impactful. Each discussant also mentioned other areas of bioinformatics that have been impacted by statistical researchers that we did not highlight in the original paper. In response, we will first summarize discuss these general themes, and then respond to specific comments of each discussant, and finally talk about the additional areas of bioinformatics impacted by statisticians that were mentioned by the reviewers.

Identifiers

PMID30034293
PMCPMC6052864

What OpenQuestion holds

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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.