Evidence map›Paper›PMID 30741379›Full record

ArticleGenes & genomics2019

Markov chain Monte Carlo simulation of a Bayesian mixture model for gene network inference.

Younhee Ko, Jaebum Kim, Sandra L Rodriguez-Zas

Abstract read
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Article in Genes & genomics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

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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.

Younhee KoDivision of Biomedical Engineering, Hankuk University of Foreign Studies, Gyeonggi-do, 17035, South Korea.
Jaebum KimDepartment of Biomedical Science and Engineering, Konkuk University, Seoul, 05029, South Korea. jbkim@konkuk.ac.kr.
Sandra L Rodriguez-ZasDepartment of Animal Sciences, University of Illinois at Urbana-Champaign, Champaign, IL, 61820, USA. rodrgzzs@illinois.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSimultaneous measurement of gene expression level for thousands of genes contains the rich information about many different aspects of biological mechanisms. A major computational challenge is to find methods to extract new biological insights from this wealth of data. Complex biological processes are often regulated under the various conditions or circumstances and associated gene interactions are dynamically changed depending on different biological contexts. Thus, inference of such dynamic relationships between genes with consideration of biological conditions is very challenging.

methodIn this study, we propose a comprehensive and integrated approach to infer the dynamic relationships between genes and evaluate this approach on three distinct gene networks.

resultsThis study demonstrates the advantage of integrating Markov chain Monte Carlo (MCMC) simulation into a Bayesian mixture model to overcome the high-dimension, low sample size (HDLSS) problem as well as to identify context-specific biological modules. Such biological modules were identified through the summarization of sampled network structures obtained from MCMC simulation.

conclusionThis novel approach gives a comprehensive understanding of the dynamically regulated biological modules.

Indexed as

AlgorithmsBayes TheoremComputational BiologyGene Expression ProfilingGene Regulatory NetworksHumansMarkov ChainsMonte Carlo MethodBayesian mixture modelGene networkMarkov chain Monte Carlo

Identifiers

PMID30741379

What OpenQuestion holds

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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.