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Journal Articles Bioinformatics Year : 2019

MetaboRank: network-based recommendation system to interpret and enrich metabolomics results

Abstract

Metabolomics has shown great potential to improve the understanding of complex diseases, potentially leading to therapeutic target identification. However, no single analytical method allows monitoring all metabolites in a sample, resulting in incomplete metabolic fingerprints. This incompleteness constitutes a stumbling block to interpretation, raising the need for methods that can enrich those fingerprints. We propose MetaboRank, a new solution inspired by social network recommendation systems for the identification of metabolites potentially related to a metabolic fingerprint. MetaboRank method had been used to enrich metabolomics data obtained on cerebrospinal fluid samples from patients suffering from hepatic encephalopathy. MetaboRank successfully recommended metabolites not present in the original fingerprint. The quality of recommendations was evaluated by using literature automatic search, in order to check that recommended metabolites could be related to the disease. Complementary mass spectrometry experiments and raw data analysis were performed to confirm these suggestions. In particular, MetaboRank recommended the overlooked α-ketoglutaramate as a metabolite which should be added to the metabolic fingerprint of hepatic encephalopathy, thus suggesting that metabolic fingerprints enhancement can provide new insight on complex diseases.
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Dates and versions

hal-02627792 , version 1 (01-02-2024)

Licence

Attribution - NonCommercial

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Clément Frainay, Sandrine Aros, Maxime Chazalviel, Thomas Garcia, Florence Vinson, et al.. MetaboRank: network-based recommendation system to interpret and enrich metabolomics results. Bioinformatics, 2019, 35 (2), pp.274-283. ⟨10.1093/bioinformatics/bty577⟩. ⟨hal-02627792⟩
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