Evidence map›Paper›PMID 33824624›Full record

ArticleStatistical modelling2019

Identifying Dynamical Time Series Model Parameters from Equilibrium Samples, with Application to Gene Regulatory Networks.

William Chad Young, Ka Yee Yeung, Adrian E Raftery

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Statistical modelling, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

William Chad YoungVaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Ka Yee YeungInstitute of Technology, University of Washington, Tacoma, WA, USA.
Adrian E RafteryDepartment of Statistics, University of Washington, Seattle, WA, USA.

Funding

Data Coordination and Integration Center for LINCS-BD2KU54HL127624 · NHLBI · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI MA'AYAN, AVI, SCHURER, STEPHAN C · 2014 to 2019
$23.5M
Statistical Methods for Population ProjectionsR01HD070936 · NICHD · UNIVERSITY OF WASHINGTON · PI RAFTERY, ADRIAN E · 2012 to 2021
$3.3M
Sexually Transmitted Infection Epidemics: Bayesian Prevalence & Size EstimateR01HD054511 · NICHD · UNIVERSITY OF WASHINGTON · PI RAFTERY, ADRIAN E · 2007 to 2016
$3.2M
NHLBI NIH HHS U54 HL127624NICHD NIH HHS R01 HD054511NICHD NIH HHS R01 HD070936
6 · The paper itself

Abstract

Gene regulatory network reconstruction is an essential task of genomics in order to further our understanding of how genes interact dynamically with each other. The most readily available data, however, are from steady state observations. These data are not as informative about the relational dynamics between genes as knockout or over-expression experiments, which attempt to control the expression of individual genes. We develop a new framework for network inference using samples from the equilibrium distribution of a vector autoregressive (VAR) time-series model which can be applied to steady state gene expression data. We explore the theoretical aspects of our method and apply the method to synthetic gene expression data generated using GeneNetWeaver.

Indexed as

Gene networksNetwork reconstructionTime seriesVAR equilibrium

Identifiers

PMID33824624
PMCPMC8021096

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Registered trials

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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.