ArticleInternational journal of bioinformatics research and applications2010
Discovery of gene network variability across samples representing multiple classes.
Article in International journal of bioinformatics research and applications, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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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
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Who cites it
3 citing papers in PubMed.
- Markov chain Monte Carlo simulation of a Bayesian mixture model for gene network inference.Genes & genomics · 2019Article
- Microarray analysis of natural socially regulated plasticity in circadian rhythms of honey bees.Journal of biological rhythms · 2012Article
- A model-based method for gene dependency measurement.PloS one · 2012Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Gene networks have been predicted using the expression profiles from microarray experiments that include multiple samples representing each of several classes or states (e.g., treatments, developmental stages, health status). A framework that integrates Bayesian networks, mixture of gene co-expression models and clustering is proposed to further mine information from the variation of samples within and across classes and enhance the understanding of gene networks. The approach was evaluated on two independent pathways using data from two microarray experiments. Our algorithm succeeded on reconstructing the topology of the gene pathways when benchmarked against empirical reports and randomised data sets. The majority or all the samples within a class shared the same co-expression model and were classified within the corresponding class. Our approach uncovered both gene relationships and profiles that are unique to a particular class or shared across classes.
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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.