ArticleJournal of biomedical informatics2009
Use of Bayesian networks to probabilistically model and improve the likelihood of validation of microarray findings by RT-PCR.
Article in Journal of biomedical informatics, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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.
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.
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Who cites it
4 citing papers in PubMed.
- TM4SF1-Directed Antibody-Drug Conjugates Selectively Destroy Newly Formed Blood Vessels Induced by VEGF-A.International journal of molecular sciences · 2026Article
- Cable Cars to the Nucleus: TM4SF1-Enriched Microdomains Conduct Signaling in Endothelial Cells for Blood Vessel Formation.International journal of molecular sciences · 2025Article
- TM4SF1 is a molecular facilitator that distributes cargo proteins intracellularly in endothelial cells in support of blood vessel formation.Journal of cell communication and signaling · 2024Article
- Marco Ramoni: an appreciation of academic achievement.Journal of the American Medical Informatics Association : JAMIAArticle
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Though genome-wide technologies, such as microarrays, are widely used, data from these methods are considered noisy; there is still varied success in downstream biological validation. We report a method that increases the likelihood of successfully validating microarray findings using real time RT-PCR, including genes at low expression levels and with small differences. We use a Bayesian network to identify the most relevant sources of noise based on the successes and failures in validation for an initial set of selected genes, and then improve our subsequent selection of genes for validation based on eliminating these sources of noise. The network displays the significant sources of noise in an experiment, and scores the likelihood of validation for every gene. We show how the method can significantly increase validation success rates. In conclusion, in this study, we have successfully added a new automated step to determine the contributory sources of noise that determine successful or unsuccessful downstream biological validation.
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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.