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Oxford University Press (OUP), Bioinformatics, 17(30), p. i461-i467

DOI: 10.1093/bioinformatics/btu455

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Stronger findings for metabolomics through Bayesian modeling of multiple peaks and compound correlations

Journal article published in 2014 by Tommi Suvitaival ORCID, Simon Rogers ORCID, Samuel Kaski
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Abstract

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, which in many cases renders them unreliable as biomarkers. However, the levels of similar compounds are typically highly correlated, which is a phenomenon that we model in this work.