ArticleBMC bioinformatics2009
Leveraging existing biological knowledge in the identification of candidate genes for facial dysmorphology.
Article in BMC bioinformatics, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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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.
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Who cites it
11 citing papers in PubMed.
- Knockout of the gene encoding the extracellular matrix protein SNED1 results in early neonatal lethality and craniofacial malformations.Developmental dynamics : an official publication of the American Association of Anatomists · 2021Article
- Systems biology of facial development: contributions of ectoderm and mesenchyme.Developmental biology · 2017Article
- Finding gene regulatory network candidates using the gene expression knowledge base.BMC bioinformatics · 2014Article
- Synergizing genomic analysis with biological knowledge to identify and validate novel genes in pancreatic development.Pancreas · 2012Article
- Protein carbonylation in a murine model for early alcoholic liver disease.Chemical research in toxicology · 2012Article
- Data driven linear algebraic methods for analysis of molecular pathways: application to disease progression in shock/trauma.Journal of biomedical informatics · 2012Article
- Cross-product extensions of the Gene Ontology.Journal of biomedical informatics · 2011Article
- Spatial and temporal analysis of gene expression during growth and fusion of the mouse facial prominences.PloS one · 2009Article
- Consistent visualizations of changing knowledge.Summit on translational bioinformatics · 2009Article
- Biomedical discovery acceleration, with applications to craniofacial development.PLoS computational biology · 2009Article
- Selected proceedings of the First Summit on Translational Bioinformatics 2008.BMC bioinformatics · 2009Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
backgroundIn response to the frequently overwhelming output of high-throughput microarray experiments, we propose a methodology to facilitate interpretation of biological data in the context of existing knowledge. Through the probabilistic integration of explicit and implicit data sources a functional interaction network can be constructed. Each edge connecting two proteins is weighted by a confidence value capturing the strength and reliability of support for that interaction given the combined data sources. The resulting network is examined in conjunction with expression data to identify groups of genes with significant temporal or tissue specific patterns. In contrast to unstructured gene lists, these networks often represent coherent functional groupings.
resultsBy linking from shared functional categorizations to primary biological resources we apply this method to craniofacial microarray data, generating biologically testable hypotheses and identifying candidate genes for craniofacial development.
conclusionThe novel methodology presented here illustrates how the effective integration of pre-existing biological knowledge and high-throughput experimental data drives biological discovery and hypothesis generation.
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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.