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Stronger findings for metabolomics through Bayesian modeling of multiple peaks and compound correlations
Motivation: Data analysis for metabolomics suffers from uncertainty because of the noisy measurement technology and the small sample size of experiments. Noise and the small sample size lead to a high probability of false findings. Further, individual compounds have natural variation between samples...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Oxford University Press
2014
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4147908/ https://www.ncbi.nlm.nih.gov/pubmed/25161234 http://dx.doi.org/10.1093/bioinformatics/btu455 |